Data mining projects topics for java and dot net

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Data Mining for java/ dot net
1.A Supermodularity-Based Differential Privacy Preserving
Algorithm for Data Anonymization.
Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations
always apply a set of transformations on their data before releasing it. While determining
the best set of transformations has been the focus of extensive work in the database
community, most of this work suffered from one or both of the following major problems:
scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for
privacy that ensures that the system essentially behaves the same way regardless of
whether any individual is included in the database. In this paper, we address both scalability
and privacy risk of data anonymization. We propose a scalable algorithm that meets
differential privacy when applying a specific random sampling. The contribution of the paper
is two-fold: 1) we propose a personalized anonymization technique based on an aggregate
formulation and prove that it can be implemented in polynomial time; and 2) we show that
combining the proposed aggregate formulation with specific sampling gives an
anonymization algorithm that satisfies differential privacy. Our results rely heavily on
exploring the supermodularity properties of the risk function, which allow us to employ
techniques from convex optimization. Through experimental studies we compare our
proposed algorithm with other anonymization schemes in terms of both time and privacy
risk.
2.A Data-Mining Model for Protection of FACTS-Based
Transmission Line.
Synopsis:
This paper presents a data-mining model for fault-zone identification of a flexible ac
transmission systems (FACTS)-based transmission line including a thyristor-controlled
series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble
decision trees. Given the randomness in the ensemble of decision trees stacked inside the
random forests model, it provides effective decision on fault-zone identification. Half-cycle
postfault current and voltage samples from the fault inception are used as an input vector
against target output “1” for the fault after TCSC/UPFC and “ - 1” for the fault before
TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data
with wide variations in operating parameters of the power system network, including noisy
environment providing a reliability measure of 99% with faster response time (3/4th cycle
from fault inception). The results of the presented approach using the RF model indicate
reliable identification of the fault zone in FACTS-based transmission lines.

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3.A Temporal Pattern Search Algorithm for Personal
History Event Visualization.
Synopsis:
We present Temporal Pattern Search (TPS), a novel algorithm for searching for temporal
patterns of events in historical personal histories. The traditional method of searching for
such patterns uses an automaton-based approach over a single array of events, sorted by
time stamps. Instead, TPS operates on a set of arrays, where each array contains all events
of the same type, sorted by time stamps. TPS searches for a particular item in the pattern
using a binary search over the appropriate arrays. Although binary search is considerably
more expensive per item, it allows TPS to skip many unnecessary events in personal
histories. We show that TPS's running time is bounded by O(m2n lg(n)), where m is the
length of (number of events) a search pattern, and n is the number of events in a record
(history). Although the asymptotic running time of TPS is inferior to that of a
nondeterministic finite automaton (NFA) approach (O(mn)), TPS performs better than NFA
under our experimental conditions. We also show TPS is very competitive with Shift-And, a
bit-parallel approach, with real data. Since the experimental conditions we describe here
subsume the conditions under which analysts would typically use TPS (i.e., within an
interactive visualization program), we argue that TPS is an appropriate design choice for us.
4.Adaptive Cluster Distance Bounding for High
Dimensional Indexing.
Synopsis:
We consider approaches for similarity search in correlated, high-dimensional data sets,
which are derived within a clustering framework. We note that indexing by “vector
approximation” (VA-File), which was proposed as a technique to combat the “Curse of
Dimensionality,” employs scalar quantization, and hence necessarily ignores dependencies
across dimensions, which represents a source of suboptimality. Clustering, on the other
hand, exploits interdimensional correlations and is thus a more compact representation of
the data set. However, existing methods to prune irrelevant clusters are based on bounding
hyperspheres and/or bounding rectangles, whose lack of tightness compromises their
efficiency in exact nearest neighbor search. We propose a new cluster-adaptive distance
bound based on separating hyperplane boundaries of Voronoi clusters to complement our
cluster based index. This bound enables efficient spatial filtering, with a relatively small
preprocessing storage overhead and is applicable to euclidean and Mahalanobis similarity
measures. Experiments in exact nearest-neighbor set retrieval, conducted on real data sets,
show that our indexing method is scalable with data set size and data dimensionality and
outperforms several recently proposed indexes. Relative to the VA-File, over a wide range

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of quantization resolutions, it is able to reduce random IO accesses, given (roughly) the
same amount of sequential IO operations, by factors reaching 100X and more.
5.Approximate Shortest Distance Computing: A Query-
Dependent Local Landmark Scheme.
Synopsis:
Shortest distance query is a fundamental operation in large-scale networks. Many existing
methods in the literature take a landmark embedding approach, which selects a set of graph
nodes as landmarks and computes the shortest distances from each landmark to all nodes
as an embedding. To answer a shortest distance query, the precomputed distances from
the landmarks to the two query nodes are used to compute an approximate shortest
distance based on the triangle inequality. In this paper, we analyze the factors that affect
the accuracy of distance estimation in landmark embedding. In particular, we find that a
globally selected, query-independent landmark set may introduce a large relative error,
especially for nearby query nodes. To address this issue, we propose a query-dependent
local landmark scheme, which identifies a local landmark close to both query nodes and
provides more accurate distance estimation than the traditional global landmark approach.
We propose efficient local landmark indexing and retrieval techniques, which achieve low
offline indexing complexity and online query complexity. Two optimization techniques on
graph compression and graph online search are also proposed, with the goal of further
reducing index size and improving query accuracy. Furthermore, the challenge of immense
graphs whose index may not fit in the memory leads us to store the embedding in relational
database, so that a query of the local landmark scheme can be expressed with relational
operators. Effective indexing and query optimization mechanisms are designed in this
context. Our experimental results on large-scale social networks and road networks
demonstrate that the local landmark scheme reduces the shortest distance estimation error
significantly when compared with global landmark embedding and the state-of-the-art
sketch-based embedding.
6.A Fast Clustering-Based Feature Subset Selection
Algorithm for High Dimensional Data.
Synopsis:
Feature selection involves identifying a subset of the most useful features that produces
compatible results as the original entire set of features. A feature selection algorithm may
be evaluated from both the efficiency and effectiveness points of view. While the efficiency
concerns the time required to find a subset of features, the effectiveness is related to the
quality of the subset of features. Based on these criteria, a fast clustering-based feature

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selection algorithm (FAST) is proposed and experimentally evaluated in this paper. The
FAST algorithm works in two steps. In the first step, features are divided into clusters by
using graph-theoretic clustering methods. In the second step, the most representative
feature that is strongly related to target classes is selected from each cluster to form a
subset of features. Features in different clusters are relatively independent, the clustering-
based strategy of FAST has a high probability of producing a subset of useful and
independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-
spanning tree (MST) clustering method. The efficiency and effectiveness of the FAST
algorithm are evaluated through an empirical study. Extensive experiments are carried out
to compare FAST and several representative feature selection algorithms, namely, FCBF,
ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers,
namely, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1,
and the rule-based RIPPER before and after feature selection. The results, on 35 publicly
available real-world high-dimensional image, microarray, and text data, demonstrate that
the FAST not only produces smaller subsets of features but also improves the
performances of the four types of classifiers.
7.Advance Mining of Temporal High Utility Itemset.
Synopsis:
The stock market domain is a dynamic and unpredictable environment. Traditional
techniques, such as fundamental and technical analysis can provide investors with some
tools for managing their stocks and predicting their prices. However, these techniques
cannot discover all the possible relations between stocks and thus there is a need for a
different approach that will provide a deeper kind of analysis. Data mining can be used
extensively in the financial markets and help in stock-price forecasting. Therefore, we
propose in this paper a portfolio management solution with business intelligence
characteristics. We know that the temporal high utility itemsets are the itemsets with support
larger than a pre-specified threshold in current time window of data stream. Discovery of
temporal high utility itemsets is an important process for mining interesting patterns like
association rules from data streams. We proposed the novel algorithm for temporal
association mining with utility approach. This make us to find the temporal high utility
itemset which can generate less candidate itemsets.
8.Data Leakage Detection.
Synopsis:

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We study the following problem: A data distributor has given sensitive data to a set of
supposedly trusted agents (third parties). Some of the data are leaked and found in an
unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess
the likelihood that the leaked data came from one or more agents, as opposed to having
been independently gathered by other means. We propose data allocation strategies
(across the agents) that improve the probability of identifying leakages. These methods do
not rely on alterations of the released data (e.g., watermarks). In some cases, we can also
inject “realistic but fake” data records to further improve our chances of detecting leakage
and identifying the guilty party.
9.Best Keyword Cover Search.
Synopsis:
It is common that the objects in a spatial database (e.g., restaurants/hotels) are
associated with keyword(s) to indicate their businesses/services/features. An
interesting problem known as Closest Keywords search is to query objects, called
keyword cover, which together cover a set of query keywords and have the
minimum inter-objects distance. In recent years, we observe the increasing
availability and importance of keyword rating in object evaluation for the better
decision making. This motivates us to investigate a generic version of Closest
Keywords search called Best Keyword Cover which considers inter-objects distance
as well as the keyword rating of objects. The baseline algorithm is inspired by the
methods of Closest Keywords search which is based on exhaustively combining
objects from different query keywords to generate candidate keyword covers. When
the number of query keywords increases, the performance of the baseline algorithm
drops dramatically as a result of massive candidate keyword covers generated. To
attack this drawback, this work proposes a much more scalable algorithm called
keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline
algorithm, keyword-NNE algorithm significantly reduces the number of candidate
keyword covers generated. The in-depth analysis and extensive experiments on real
data sets have justified the superiority of our keyword-NNE algorithm.
10.A Generalized Flow-Based Method for Analysis of
Implicit Relationships on Wikipedia.
Synopsis:

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We focus on measuring relationships between pairs of objects in Wikipedia whose pages
can be regarded as individual objects. Two kinds of relationships between two objects exist:
in Wikipedia, an explicit relationship is represented by a single link between the two pages
for the objects, and an implicit relationship is represented by a link structure containing the
two pages. Some of the previously proposed methods for measuring relationships are
cohesion-based methods, which underestimate objects having high degrees, although such
objects could be important in constituting relationships in Wikipedia. The other methods are
inadequate for measuring implicit relationships because they use only one or two of the
following three important factors: distance, connectivity, and cocitation. We propose a new
method using a generalized maximum flow which reflects all the three factors and does not
underestimate objects having high degree. We confirm through experiments that our
method can measure the strength of a relationship more appropriately than these previously
proposed methods do. Another remarkable aspect of our method is mining elucidatory
objects, that is, objects constituting a relationship. We explain that mining elucidatory
objects would open a novel way to deeply understand a relationship.
11.An Exploration of Improving Collaborative
Recommender Systems via User-Item Subgroups.
Synopsis:
Collaborative filtering (CF) is one of the most successful recommendation approaches. It
typically associates a user with a group of like-minded users based on their preferences
over all the items, and recommends to the user those items enjoyed by others in the group.
However we find that two users with similar tastes on one item subset may have totally
different tastes on another set. In other words, there exist many user-item subgroups each
consisting of a subset of items and a group of like-minded users on these items. It is more
natural to make preference predictions for a user via the correlated subgroups than the
entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the
Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we
propose an unified framework to extend the traditional CF algorithms by utilizing the
subgroups information for improving their top-N recommendation performance. Our
approach can be seen as an extension of traditional clustering CF models. Systematic
experiments on three real world data sets have demonstrated the effectiveness of our
proposed approach.
12.Decision Trees for Uncertain Data.
Synopsis:
Traditional decision tree classifiers work with data whose values are known and precise. We
extend such classifiers to handle data with uncertain information. Value uncertainty arises in

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many applications during the data collection process. Example sources of uncertainty include
measurement/quantization errors, data staleness, and multiple repeated measurements. With
uncertainty, the value of a data item is often represented not by one single value, but by multiple
values forming a probability distribution. Rather than abstracting uncertain data by statistical
derivatives (such as mean and median), we discover that the accuracy of a decision tree
classifier can be much improved if the "complete information" of a data item (taking into account
the probability density function (pdf)) is utilized. We extend classical decision tree building
algorithms to handle data tuples with uncertain values. Extensive experiments have been
conducted which show that the resulting classifiers are more accurate than those using value
averages. Since processing pdfs is computationally more costly than processing single values
(e.g., averages), decision tree construction on uncertain data is more CPU demanding than that
for certain data. To tackle this problem, we propose a series of pruning techniques that can
greatly improve construction efficiency.
13.Building Conﬁdential and Efﬁcient Query Services in the
Cloud with RASP Data Perturbation..
Synopsis:
With the wide deployment of public cloud computing infrastructures, using clouds to host
data query services has become an appealing solution for the advantages on scalability and
cost-saving. However, some data might be sensitive that the data owner does not want to
move to the cloud unless the data confidentiality and query privacy are guaranteed. On the
other hand, a secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of cloud computing.
We propose the random space perturbation (RASP) data perturbation method to provide
secure and efficient range query and kNN query services for protected data in the cloud.
The RASP data perturbation method combines order preserving encryption, dimensionality
expansion, random noise injection, and random projection, to provide strong resilience to
attacks on the perturbed data and queries. It also preserves multidimensional ranges, which
allows existing indexing techniques to be applied to speedup range query processing. The
kNN-R algorithm is designed to work with the RASP range query algorithm to process the
kNN queries. We have carefully analyzed the attacks on data and queries under a precisely
defined threat model and realistic security assumptions. Extensive experiments have been
conducted to show the advantages of this approach on efficiency and security.
14.A Methodology for Direct and Indirect Discrimination
Prevention in Data Mining.
Synopsis:

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Data mining is an increasingly important technology for extracting useful knowledge hidden
in large collections of data. There are, however, negative social perceptions about data
mining, among which potential privacy invasion and potential discrimination. The latter
consists of unfairly treating people on the basis of their belonging to a specific group.
Automated data collection and data mining techniques such as classification rule mining
have paved the way to making automated decisions, like loan granting/denial, insurance
premium computation, etc. If the training data sets are biased in what regards discriminatory
(sensitive) attributes like gender, race, religion, etc., discriminatory decisions may ensue.
For this reason, anti-discrimination techniques including discrimination discovery and
prevention have been introduced in data mining. Discrimination can be either direct or
indirect. Direct discrimination occurs when decisions are made based on sensitive
attributes. Indirect discrimination occurs when decisions are made based on nonsensitive
attributes which are strongly correlated with biased sensitive ones. In this paper, we tackle
discrimination prevention in data mining and propose new techniques applicable for direct
or indirect discrimination prevention individually or both at the same time. We discuss how
to clean training data sets and outsourced data sets in such a way that direct and/or indirect
discriminatory decision rules are converted to legitimate (nondiscriminatory) classification
rules. We also propose new metrics to evaluate the utility of the proposed approaches and
we compare these approaches. The experimental evaluations demonstrate that the
proposed techniques are effective at removing direct and/or indirect discrimination biases in
the original data set while preserving data quality.
15.Anomaly Detection for Discrete Sequences: A Survey.
Synopsis:
This survey attempts to provide a comprehensive and structured overview of the existing
research for the problem of detecting anomalies in discrete/symbolic sequences. The
objective is to provide a global understanding of the sequence anomaly detection problem
and how existing techniques relate to each other. The key contribution of this survey is the
classification of the existing research into three distinct categories, based on the problem
formulation that they are trying to solve. These problem formulations are: 1) identifying
anomalous sequences with respect to a database of normal sequences; 2) identifying an
anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence
whose frequency of occurrence is anomalous. We show how each of these problem
formulations is characteristically distinct from each other and discuss their relevance in
various application domains. We review techniques from many disparate and disconnected
application domains that address each of these formulations. Within each problem
formulation, we group techniques into categories based on the nature of the underlying
algorithm. For each category, we provide a basic anomaly detection technique, and show
how the existing techniques are variants of the basic technique. This approach shows how
different techniques within a category are related or different from each other. Our

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categorization reveals new variants and combinations that have not been investigated
before for anomaly detection. We also provide a discussion of relative strengths and
weaknesses of different techniques. We show how techniques developed for one problem
formulation can be adapted to solve a different formulation, thereby providing several novel
adaptations to solve the different problem formulations. We also highlight the applicability of
the techniques that handle discrete sequences to other related areas such as online
anomaly detection and time series anomaly detection.
16. Discovering Conditional Functional Dependencies.
Synopsis:
This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs
are a recent extension of functional dependencies (FDs) by supporting patterns of
semantically related constants, and can be used as rules for cleaning relational data.
However, finding quality CFDs is an expensive process that involves intensive manual
effort. To effectively identify data cleaning rules, we develop techniques for discovering
CFDs from relations. Already hard for traditional FDs, the discovery problem is more difficult
for CFDs. Indeed, mining patterns in CFDs introduces new challenges. We provide three
methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for
mining closed item sets, and is used to discover constant CFDs, namely, CFDs with
constant patterns only. Constant CFDs are particularly important for object identification,
which is essential to data cleaning and data integration. The other two algorithms are
developed for discovering general CFDs. One algorithm, referred to as CTANE, is a
levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other,
referred to as FastCFD, is based on the depth-first approach used in FastFD, a method for
discovering FDs. It leverages closed-item-set mining to reduce the search space. As
verified by our experimental study, CFDMiner can be multiple orders of magnitude faster
than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given
relation is large, but it does not scale well with the arity of the relation. FastCFD is far more
efficient than CTANE when the arity of the relation is large; better still, leveraging
optimization based on closed-item-set mining, FastCFD also scales well with the size of the
relation. These algorithms provide a set of cleaning-rule discovery tools for users to choose
for different applications.
17.Capturing Telic/Atelic Temporal Data Semantics:
Generalizing Conventional Conceptual Models.
Synopsis:

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Time provides context for all our experiences, cognition, and coordinated collective action.
Prior research in linguistics, artificial intelligence, and temporal databases suggests the
need to differentiate between temporal facts with goal-related semantics (i.e., telic) from
those are intrinsically devoid of culmination (i.e., atelic). To differentiate between telic and
atelic data semantics in conceptual database design, we propose an annotation-based
temporal conceptual model that generalizes the semantics of a conventional conceptual
model. Our temporal conceptual design approach involves: 1) capturing "what" semantics
using a conventional conceptual model; 2) employing annotations to differentiate between
telic and atelic data semantics that help capture "when" semantics; 3) specifying temporal
constraints, specifically nonsequenced semantics, in the temporal data dictionary as
metadata. Our proposed approach provides a mechanism to represent telic/atelic temporal
semantics using temporal annotations. We also show how these semantics can be formally
defined using constructs of the conventional conceptual model and axioms in first-order
logic. Via what we refer to as the "semantics of composition," i.e., semantics implied by the
interaction of annotations, we illustrate the logical consequences of representing telic/atelic
data semantics during temporal conceptual design.
18.A New Algorithm for Inferring User Search Goals with
Feedback Sessions.
Synopsis:
For a broad-topic and ambiguous query, different users may have different search goals
when they submit it to a search engine. The inference and analysis of user search goals
can be very useful in improving search engine relevance and user experience. In this paper,
we propose a novel approach to infer user search goals by analyzing search engine query
logs. First, we propose a framework to discover different user search goals for a query by
clustering the proposed feedback sessions. Feedback sessions are constructed from user
click-through logs and can efficiently reflect the information needs of users. Second, we
propose a novel approach to generate pseudo-documents to better represent the feedback
sessions for clustering. Finally, we propose a new criterion )“Classified Average Precision
(CAP)” to evaluate the performance of inferring user search goals. Experimental results are
presented using user click-through logs from a commercial search engine to validate the
effectiveness of our proposed methods.
19. Automatic Discovery of Association Orders between
Name and Aliases from the Web using Anchor Texts-based
Co- occurrences.
Synopsis:

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Many celebrities and experts from various fields may have been referred by not only their
personal names but also by their aliases on web. Aliases are very important in information
retrieval to retrieve complete information about a personal name from the web, as some of
the web pages of the person may also be referred by his aliases. The aliases for a personal
name are extracted by previously proposed alias extraction method. In information retrieval,
the web search engine automatically expands the search query on a person name by
tagging his aliases for complete information retrieval thereby improving recall in relation
detection task and achieving a significant mean reciprocal rank (MRR) of search engine.
For the further substantial improvement on recall and MRR from the previously proposed
methods, our proposed method will order the aliases based on their associations with the
name using the definition of anchor texts-based co-occurrences between name and aliases
in order to help the search engine tag the aliases according to the order of associations.
The association orders will automatically be discovered by creating an anchor texts-based
co-occurrence graph between name and aliases. Ranking support vector machine (SVM)
will be used to create connections between name and aliases in the graph by performing
ranking on anchor texts-based co-occurrence measures. The hop distances between nodes
in the graph will lead to have the associations between name and aliases. The hop
distances will be found by mining the graph. The proposed method will outperform
previously proposed methods, achieving substantial growth on recall and MRR.
20.Effective Navigation of Query Results Based on Concept
Hierarchies.
Synopsis:
Search queries on biomedical databases, such as PubMed, often return a large number of
results, only a small subset of which is relevant to the user. Ranking and categorization,
which can also be combined, have been proposed to alleviate this information overload
problem. Results categorization for biomedical databases is the focus of this work. A natural
way to organize biomedical citations is according to their MeSH annotations. MeSH is a
comprehensive concept hierarchy used by PubMed. In this paper, we present the BioNav
system, a novel search interface that enables the user to navigate large number of query
results by organizing them using the MeSH concept hierarchy. First, the query results are
organized into a navigation tree. At each node expansion step, BioNav reveals only a small
subset of the concept nodes, selected such that the expected user navigation cost is
minimized. In contrast, previous works expand the hierarchy in a predefined static manner,
without navigation cost modeling. We show that the problem of selecting the best concepts
to reveal at each node expansion is NP-complete and propose an efficient heuristic as well
as a feasible optimal algorithm for relatively small trees. We show experimentally that
BioNav outperforms state-of-the-art categorization systems by up to an order of magnitude,
with respect to the user navigation cost. BioNav for the MEDLINE database is available at
http://db.cse.buffalo.edu/bionav.

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21.Dealing With Concept Drifts in Process MiningServices.
Synopsis:
Although most business processes change over time, contemporary process mining
techniques tend to analyze these processes as if they are in a steady state. Processes may
change suddenly or gradually. The drift may be periodic (e.g., because of seasonal
influences) or one-of-a-kind (e.g., the effects of new legislation). For the process
management, it is crucial to discover and understand such concept drifts in processes. This
paper presents a generic framework and specific techniques to detect when a process
changes and to localize the parts of the process that have changed. Different features are
proposed to characterize relationships among activities. These features are used to
discover differences between successive populations. The approach has been implemented
as a plug-in of the ProM process mining framework and has been evaluated using both
simulated event data exhibiting controlled concept drifts and real-life event data from a
Dutch municipality.
22. A Probabilistic Approach to String Transformation.
Synopsis:
Many problems in natural language processing, data mining, information retrieval, and
bioinformatics can be formalized as string transformation, which is a task as follows. Given
an input string, the system generates the k most likely output strings corresponding to the
input string. This paper proposes a novel and probabilistic approach to string
transformation, which is both accurate and efficient. The approach includes the use of a log
linear model, a method for training the model, and an algorithm for generating the top k
candidates, whether there is or is not a predefined dictionary. The log linear model is
defined as a conditional probability distribution of an output string and a rule set for the
transformation conditioned on an input string. The learning method employs maximum
likelihood estimation for parameter estimation. The string generation algorithm based on
pruning is guaranteed to generate the optimal top k candidates. The proposed method is
applied to correction of spelling errors in queries as well as reformulation of queries in web
search. Experimental results on large scale data show that the proposed approach is very
accurate and efficient improving upon existing methods in terms of accuracy and efficiency
in different settings.
23. Confucius A Tool Supporting Collaborative Scientific
Workflow Composition.
Synopsis:

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Modern scientific data management and analysis usually rely on multiple scientists with
diverse expertise. In recent years, such a collaborative effort is often structured and
automated by a data flow-oriented process called scientific workflow. However, such
workflows may have to be designed and revised among multiple scientists over a long time
period. Existing workbenches are single user-oriented and do not support scientific workflow
application development in a "collaborative fashion". In this paper, we report our research
on the enabling techniques in the aspects of collaboration provenance management and
reproduciability. Based on a scientific collaboration ontology, we propose a service-oriented
collaboration model supported by a set of composable collaboration primitives and patterns.
The collaboration protocols are then applied to support effective concurrency control in the
process of collaborative workflow composition. We also report the design and development
of Confucius, a service-oriented collaborative scientific workflow composition tool that
extends an open-source, single-user development environment.
24Extended XML Tree Pattern Matching: Theories and
Algorithms.
Synopsis:
As business and enterprises generate and exchange XML data more often, there is an
increasing need for efficient processing of queries on XML data. Searching for the
occurrences of a tree pattern query in an XML database is a core operation in XML query
processing. Prior works demonstrate that holistic twig pattern matching algorithm is an
efficient technique to answer an XML tree pattern with parent-child (P-C) and ancestor-
descendant (A-D) relationships, as it can effectively control the size of intermediate results
during query processing. However, XML query languages (e.g., XPath and XQuery) define
more axes and functions such as negation function, order-based axis, and wildcards. In this
paper, we research a large set of XML tree pattern, called extended XML tree pattern,
which may include P-C, A-D relationships, negation functions, wildcards, and order
restriction. We establish a theoretical framework about “matching cross” which
demonstrates the intrinsic reason in the proof of optimality on holistic algorithms. Based on
our theorems, we propose a set of novel algorithms to efficiently process three categories of
extended XML tree patterns. A set of experimental results on both real-life and synthetic
data sets demonstrate the effectiveness and efficiency of our proposed theories and
algorithms.
25. Efﬁcient Ranking on Entity Graphs with Personalized
Relationships.
Synopsis:

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Authority flow techniques like PageRank and ObjectRank can provide personalized ranking of
typed entity-relationship graphs. There are two main ways to personalize authority flow ranking:
Node-based personalization, where authority originates from a set of user-specific nodes; edge-
based personalization, where the importance of different edge types is user-specific. We
propose the first approach to achieve efficient edge-based personalization using a combination
of precomputation and runtime algorithms. In particular, we apply our method to ObjectRank,
where a personalized weight assignment vector (WAV) assigns different weights to each edge
type or relationship type. Our approach includes a repository of rankings for various WAVs. We
consider the following two classes of approximation: (a) SchemaApprox is formulated as a
distance minimization problem at the schema level; (b) DataApprox is a distance minimization
problem at the data graph level. SchemaApprox is not robust since it does not distinguish
between important and trivial edge types based on the edge distribution in the data graph. In
contrast, DataApprox has a provable error bound. Both SchemaApprox and DataApprox are
expensive so we develop efficient heuristic implementations, ScaleRank and PickOne
respectively. Extensive experiments on the DBLP data graph show that ScaleRank provides a
fast and accurate personalized authority flow ranking.
26.A Survey of XML Tree Patterns.
Synopsis:
With XML becoming a ubiquitous language for data interoperability purposes in various
domains, efficiently querying XML data is a critical issue. This has lead to the design of
algebraic frameworks based on tree-shaped patterns akin to the tree-structured data model
of XML. Tree patterns are graphic representations of queries over data trees. They are
actually matched against an input data tree to answer a query. Since the turn of the 21st
century, an astounding research effort has been focusing on tree pattern models and
matching optimization (a primordial issue). This paper is a comprehensive survey of these
topics, in which we outline and compare the various features of tree patterns. We also
review and discuss the two main families of approaches for optimizing tree pattern
matching, namely pattern tree minimization and holistic matching. We finally present actual
tree pattern-based developments, to provide a global overview of this significant research
topic.
27. Coupled Behavior Analysis for Capturing Coupling
Relationships in Group-based Market Manipulations.

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Synopsis:
Coupled behaviors, which refer to behaviors having some relationships between them, are
usually seen in many real-world scenarios, especially in stock markets. Recently, the
coupled hidden Markov model (CHMM)-based coupled behavior analysis has been
proposed to consider the coupled relationships in a hidden state space. However, it requires
aggregation of the behavioral data to cater for the CHMM modeling, which may overlook the
couplings within the aggregated behaviors to some extent. In addition, the Markov
assumption limits its capability to capturing temporal couplings. Thus, this paper proposes a
novel graph-based framework for detecting abnormal coupled behaviors. The proposed
framework represents the coupled behaviors in a graph view without aggregating the
behavioral data and is flexible to capture richer coupling information of the behaviors (not
necessarily temporal relations). On top of that, the couplings are learned via relational
learning methods and an efficient anomaly detection algorithm is proposed as well.
Experimental results on a real-world data set in stock markets show that the proposed
framework outperforms the CHMM-based one in both technical and business measures.
28.Group Enclosing Queries..
Synopsis:
Given a set of points P and a query set Q, a group enclosing query (Geq) fetches the point
p* ∈ P such that the maximum distance of p* to all points in Q is minimized. This problem is
equivalent to the Min-Max case (minimizing the maximum distance) of aggregate nearest
neighbor queries for spatial databases. This work first designs a new exact solution by
exploring new geometric insights, such as the minimum enclosing ball, the convex hull, and
the furthest voronoi diagram of the query group. To further reduce the query cost, especially
when the dimensionality increases, we turn to approximation algorithms. Our main
approximation algorithm has a worst case √2-approximation ratio if one can find the exact
nearest neighbor of a point. In practice, its approximation ratio never exceeds 1.05 for a
large number of data sets up to six dimensions. We also discuss how to extend it to higher
dimensions (up to 74 in our experiment) and show that it still maintains a very good
approximation quality (still close to 1) and low query cost. In fixed dimensions, we extend
the √2-approximation algorithm to get a (1 + ε)-approximate solution for the Geq problem.
Both approximation algorithms have O(log N + M) query cost in any fixed dimension, where
N and M are the sizes of the data set P and query group Q. Extensive experiments on both
synthetic and real data sets, up to 10 million points and 74 dimensions, confirm the
efficiency, effectiveness, and scalability of the proposed algorithms, especially their
significant improvement over the state-of-the-art method.

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29.Facilitating Document Annotation using Content and
Querying Value.
Synopsis:
A large number of organizations today generate and share textual descriptions of their
products, services, and actions. Such collections of textual data contain significant amount
of structured information, which remains buried in the unstructured text. While information
extraction algorithms facilitate the extraction of structured relations, they are often
expensive and inaccurate, especially when operating on top of text that does not contain
any instances of the targeted structured information. We present a novel alternative
approach that facilitates the generation of the structured metadata by identifying documents
that are likely to contain information of interest and this information is going to be
subsequently useful for querying the database. Our approach relies on the idea that
humans are more likely to add the necessary metadata during creation time, if prompted by
the interface; or that it is much easier for humans (and/or algorithms) to identify the
metadata when such information actually exists in the document, instead of naively
prompting users to fill in forms with information that is not available in the document. As a
major contribution of this paper, we present algorithms that identify structured attributes that
are likely to appear within the document, by jointly utilizing the content of the text and the
query workload. Our experimental evaluation shows that our approach generates superior
results compared to approaches that rely only on the textual content or only on the query
workload, to identify attributes of interest.
30.A System to Filter Unwanted Messages from OSN User
Walls.
Synopsis:
One fundamental issue in today's Online Social Networks (OSNs) is to give users the ability
to control the messages posted on their own private space to avoid that unwanted content
is displayed. Up to now, OSNs provide little support to this requirement. To fill the gap, in
this paper, we propose a system allowing OSN users to have a direct control on the
messages posted on their walls. This is achieved through a flexible rule-based system, that
allows users to customize the filtering criteria to be applied to their walls, and a Machine
Learning-based soft classifier automatically labeling messages in support of content-based
filtering.
31.Creating Evolving User Behaviour Profiles
Automatically.

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Synopsis:
Knowledge about computer users is very beneficial for assisting them, predicting their future
actions or detecting masqueraders. In this paper, a new approach for creating and
recognizing automatically the behavior profile of a computer user is presented. In this case,
a computer user behavior is represented as the sequence of the commands she/he types
during her/his work. This sequence is transformed into a distribution of relevant
subsequences of commands in order to find out a profile that defines its behavior. Also,
because a user profile is not necessarily fixed but rather it evolves/changes, we propose an
evolving method to keep up to date the created profiles using an Evolving Systems
approach. In this paper, we combine the evolving classifier with a trie-based user profiling to
obtain a powerful self-learning online scheme. We also develop further the recursive
formula of the potential of a data point to become a cluster center using cosine distance,
which is provided in the Appendix. The novel approach proposed in this paper can be
applicable to any problem of dynamic/evolving user behavior modeling where it can be
represented as a sequence of actions or events. It has been evaluated on several real data
streams.
32.Load Shedding in Mobile Systems with MobiQual.
Synopsis:
In location-based, mobile continual query (CQ) systems, two key measures of quality-of-
service (QoS) are: freshness and accuracy. To achieve freshness, the CQ server must
perform frequent query reevaluations. To attain accuracy, the CQ server must receive and
process frequent position updates from the mobile nodes. However, it is often difficult to
obtain fresh and accurate CQ results simultaneously, due to 1) limited resources in
computing and communication and 2) fast-changing load conditions caused by continuous
mobile node movement. Hence, a key challenge for a mobile CQ system is: How do we
achieve the highest possible quality of the CQ results, in both freshness and accuracy, with
currently available resources? In this paper, we formulate this problem as a load shedding
one, and develop MobiQual-a QoS-aware approach to performing both update load
shedding and query load shedding. The design of MobiQual highlights three important
features. 1) Differentiated load shedding: We apply different amounts of query load
shedding and update load shedding to different groups of queries and mobile nodes,
respectively. 2) Per-query QoS specification: Individualized QoS specifications are used to
maximize the overall freshness and accuracy of the query results. 3) Low-cost adaptation:
MobiQual dynamically adapts, with a minimal overhead, to changing load conditions and
available resources. We conduct a set of comprehensive experiments to evaluate the
effectiveness of MobiQual. The results show that, through a careful combination of update
and query load shedding, the MobiQual approach leads to much higher freshness and
accuracy in the query results in all cases, compared to existing approaches that lack the

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QoS-awareness properties of MobiQual, as well as the solutions that perform query-only or
update-only load shedding.
33.Fast Nearest Neighbor Search with Keywords.
Synopsis:
Conventional spatial queries, such as range search and nearest neighbor retrieval, involve
only conditions on objects' geometric properties. Today, many modern applications call for
novel forms of queries that aim to find objects satisfying both a spatial predicate, and a
predicate on their associated texts. For example, instead of considering all the restaurants,
a nearest neighbor query would instead ask for the restaurant that is the closest among
those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently, the
best solution to such queries is based on the IR 2-tree, which, as shown in this paper, has a
few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new
access method called the spatial inverted index that extends the conventional inverted
index to cope with multidimensional data, and comes with algorithms that can answer
nearest neighbor queries with keywords in real time. As verified by experiments, the
proposed techniques outperform the IR 2-tree in query response time significantly, often by
a factor of orders of magnitude.
34.Annotating Search Results from Web Databases..
Synopsis:
An increasing number of databases have become web accessible through HTML form-
based search interfaces. The data units returned from the underlying database are usually
encoded into the result pages dynamically for human browsing. For the encoded data units
to be machine processable, which is essential for many applications such as deep web data
collection and Internet comparison shopping, they need to be extracted out and assigned
meaningful labels. In this paper, we present an automatic annotation approach that first
aligns the data units on a result page into different groups such that the data in the same
group have the same semantic. Then, for each group we annotate it from different aspects
and aggregate the different annotations to predict a final annotation label for it. An
annotation wrapper for the search site is automatically constructed and can be used to
annotate new result pages from the same web database. Our experiments indicate that the
proposed approach is highly effective.

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35.Credibility Ranking of Tweets during High Impact
Events.
Synopsis:
Twitter has evolved from being a conversation or opinion sharing medium among friends
into a platform to share and disseminate information about current events. Events in the real
world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on
Twitter is trustworthy or useful in providing information about the event. In this paper, we
analyzed the credibility of information in tweets corresponding to fourteen high impact news
events of 2011 around the globe. From the data we analyzed, on average 30% of total
tweets posted about an event contained situational information about the event while 14%
was spam. Only 17% of the total tweets posted about the event contained situational
awareness information that was credible. Using regression analysis, we identified the
important content and sourced based features, which can predict the credibility of
information in a tweet. Prominent content based features were number of unique
characters, swear words, pronouns, and emoticons in a tweet, and user based features like
the number of followers and length of username. We adopted a supervised machine
learning and relevance feedback approach using the above features, to rank tweets
according to their credibility score. The performance of our ranking algorithm significantly
enhanced when we applied re-ranking strategy. Results show that extraction of credible
information from Twitter can be automated with high confidence.
36.Making Aggregation Work in Uncertain and
Probabilistic Databases.
Synopsis:
We describe how aggregation is handled in the Trio system for uncertain and probabilistic
data. Because “exact” aggregation in uncertain databases can produce exponentially sized
results, we provide three alternatives: a low bound on the aggregate value, a high bound on
the value, and the expected value. These variants return a single result instead of a set of
possible results, and they are generally efficient to compute for both full-table and grouped
aggregation queries. We provide formal definitions and semantics and a description of our
open source implementation for single-table aggregation queries. We study the
performance and scalability of our algorithms through experiments over a large synthetic
data set. We also provide some preliminary results on aggregations over joins.
37. Incremental Affinity Propagation Clustering Based on
Message Passing.

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Synopsis:
Affinity Propagation (AP) clustering has been successfully used in a lot of clustering
problems. However, most of the applications deal with static data. This paper considers how
to apply AP in incremental clustering problems. First, we point out the difficulties in
Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve
them. Correspondingly, two IAP clustering algorithms are proposed. They are IAP clustering
based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment
(IAPNA). Five popular labeled data sets, real world time series and a video are used to test
the performance of IAPKM and IAPNA. Traditional AP clustering is also implemented to
provide benchmark performance. Experimental results show that IAPKM and IAPNA can
achieve comparable clustering performance with traditional AP clustering on all the data
sets. Meanwhile, the time cost is dramatically reduced in IAPKM and IAPNA. Both the
effectiveness and the efficiency make IAPKM and IAPNA able to be well used in
incremental clustering tasks.
38.Anomaly Detection Approach Using Hidden Markov
Model.
Synopsis:
Anomaly detection is an important problem that has been researched within diverse
research areas. Numerous methods and approaches based on Hidden Markov Model
regarding the anomaly detection have been proposed and reported in the literature.
However, the potential applications using Hidden Markov Model classification based
anomaly detection technique have not yet been fully explored and still in its infancy. This
paper investigates the capabilities the use of Hidden Markov Model in anomaly detection for
discrete sequences.
39.DDD: A New Ensemble Approach for Dealing with
Concept Drift.
Synopsis:
Online learning algorithms often have to operate in the presence of concept drifts. A recent
study revealed that different diversity levels in an ensemble of learning machines are required in
order to maintain high generalization on both old and new concepts. Inspired by this study and
based on a further study of diversity with different strategies to deal with drifts, we propose a
new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD
maintains ensembles with different diversity levels and is able to attain better accuracy than
other approaches. Furthermore, it is very robust, outperforming other drift handling approaches

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in terms of accuracy when there are false positive drift detections. In all the experimental
comparisons we have carried out, DDD always performed at least as well as other drift handling
approaches under various conditions, with very few exceptions.
40.Ranking Spatial Data by Quality Preferences.
Synopsis:
A spatial preference query ranks objects based on the qualities of features in their spatial
neighborhood. For example, using a real estate agency database of flats for lease, a
customer may want to rank the flats with respect to the appropriateness of their location,
defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital,
market, etc.) within their spatial neighborhood. Such a neighborhood concept can be
specified by the user via different functions. It can be an explicit circular region within a
given distance from the flat. Another intuitive definition is to assign higher weights to the
features based on their proximity to the flat. In this paper, we formally define spatial
preference queries and propose appropriate indexing techniques and search algorithms for
them. Extensive evaluation of our methods on both real and synthetic data reveals that an
optimized branch-and-bound solution is efficient and robust with respect to different
parameters.
41.Infrequent Weighted Itemset Mining Using Frequent
Pattern Growth.
Synopsis:
Frequent weighted itemsets represent correlations frequently holding in data in which items may
weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost
function, discovering rare data correlations is more interesting than mining frequent ones. This
paper tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted
itemset (IWI) mining problem. Two novel quality measures are proposed to drive the IWI mining
process. Furthermore, two algorithms that perform IWI and Minimal IWI mining efficiently, driven
by the proposed measures, are presented. Experimental results show efficiency and
effectiveness of the proposed approach.
42.Anomaly Detection via Online Oversampling Principal
Component Analysis.
Synopsis:

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Anomaly detection has been an important research topic in data mining and machine
learning. Many real-world applications such as intrusion or credit card fraud detection
require an effective and efficient framework to identify deviated data instances. However,
most anomaly detection methods are typically implemented in batch mode, and thus cannot
be easily extended to large-scale problems without sacrificing computation and memory
requirements. In this paper, we propose an online oversampling principal component
analysis (osPCA) algorithm to address this problem, and we aim at detecting the presence
of outliers from a large amount of data via an online updating technique. Unlike prior
principal component analysis (PCA)-based approaches, we do not store the entire data
matrix or covariance matrix, and thus our approach is especially of interest in online or
large-scale problems. By oversampling the target instance and extracting the principal
direction of the data, the proposed osPCA allows us to determine the anomaly of the target
instance according to the variation of the resulting dominant eigenvector. Since our osPCA
need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known
power method for PCA and other popular anomaly detection algorithms, our experimental
results verify the feasibility of our proposed method in terms of both accuracy and efficiency.
43.Effective Pattern Discovery for Text Mining.
Synopsis:
Many data mining techniques have been proposed for mining useful patterns in text
documents. However, how to effectively use and update discovered patterns is still an open
research issue, especially in the domain of text mining. Since most existing text mining
methods adopted term-based approaches, they all suffer from the problems of polysemy
and synonymy. Over the years, people have often held the hypothesis that pattern (or
phrase)-based approaches should perform better than the term-based ones, but many
experiments do not support this hypothesis. This paper presents an innovative and effective
pattern discovery technique which includes the processes of pattern deploying and pattern
evolving, to improve the effectiveness of using and updating discovered patterns for finding
relevant and interesting information. Substantial experiments on RCV1 data collection and
TREC topics demonstrate that the proposed solution achieves encouraging performance.
44. Relevance-based Retrieval on Hidden-Web Text
Databases without Ranking Support.

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Synopsis:
Many online or local data sources provide powerful querying mechanisms but limited
ranking capabilities. For instance, PubMed allows users to submit highly expressive
Boolean keyword queries, but ranks the query results by date only. However, a user would
typically prefer a ranking by relevance, measured by an information retrieval (IR) ranking
function. A naive approach would be to submit a disjunctive query with all query keywords,
retrieve all the returned matching documents, and then rerank them. Unfortunately, such an
operation would be very expensive due to the large number of results returned by
disjunctive queries. In this paper, we present algorithms that return the top results for a
query, ranked according to an IR-style ranking function, while operating on top of a source
with a Boolean query interface with no ranking capabilities (or a ranking capability of no
interest to the end user). The algorithms generate a series of conjunctive queries that return
only documents that are candidates for being highly ranked according to a relevance metric.
Our approach can also be applied to other settings where the ranking is monotonic on a set
of factors (query keywords in IR) and the source query interface is a Boolean expression of
these factors. Our comprehensive experimental evaluation on the PubMed database and a
TREC data set show that we achieve order of magnitude improvement compared to the
current baseline approaches.
45.A Meta-Top-Down Method for Large-Scale Hierarchical
Classification.
Synopsis:
Recent large-scale hierarchical classification tasks typically have tens of thousands of
classes on which the most widely used approach to multiclass classification--one-versus-
rest--becomes intractable due to computational complexity. The top-down methods are
usually adopted instead, but they are less accurate because of the so-called error-
propagation problem in their classifying phase. To address this problem, this paper
proposes a meta-top-down method that employs metaclassification to enhance the normal
top-down classifying procedure. The proposed method is first analyzed theoretically on
complexity and accuracy, and then applied to five real-world large-scale data sets. The
experimental results indicate that the classification accuracy is largely improved, while the
increased time costs are smaller than most of the existing approaches.
46.CloudMoV: Cloud-based Mobile Social TV.

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Synopsis:
The rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is
providing much richer contents and social interactions to users on the move. This trend
however is throttled by the limited battery lifetime of mobile devices and unstable wireless
connectivity, making the highest possible quality of service experienced by mobile users not
feasible. The recent cloud computing technology, with its rich resources to compensate for
the limitations of mobile devices and connections, can potentially provide an ideal platform
to support the desired mobile services. Tough challenges arise on how to effectively exploit
cloud resources to facilitate mobile services, especially those with stringent interaction delay
requirements. In this paper, we propose the design of a Cloud-based, novel Mobile sOcial
tV system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service)
and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of
video watching to a group of disparate mobile users who can interact socially while sharing
the video. To guarantee good streaming quality as experienced by the mobile users with
time-varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud
for video downloading and social exchanges on behalf of the user. The surrogate performs
efficient stream transcoding that matches the current connectivity quality of the mobile user.
Given the battery life as a key performance bottleneck, we advocate the use of burst
transmission from the surrogates to the mobile users, and carefully decide the burst size
which can lead to high energy efficiency and streaming quality. Social interactions among
the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient
designs of data storage with BigTable and dynamic handling of large volumes of concurrent
messages in a typical PaaS cloud. These various designs for flexible transcoding
capabilities- battery efficiency of mobile devices and spontaneous social interactivity
together provide an ideal platform for mobile social TV services. We have implemented
CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance
based on real-world experiments.
47.A Efﬁcient Anonymous Message Submission.
Synopsis:
In online surveys, many people are not willing to provide true answers due to privacy
concerns. Thus, anonymity is important for online message collection. Existing solutions let
each member blindly shuffle the submitted messages by using the IND-CCA2 secure
cryptosystem. In the end, all messages are randomly shuffled and no one knows the
message order. However, the heavy computational overhead and linear communication
rounds make it only useful for small groups. In this paper, we propose an efficient
anonymous message submission protocol aimed at a practical group size. Our protocol is
based on a simplified secret sharing scheme and a symmetric key cryptosystem. We
propose a novel method to let all members secretly aggregate their messages into a

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message vector such that a member knows nothing about other members' message
positions.We provide a theoretical proof showing that our protocol is anonymous under
malicious attacks. We then conduct a thorough analysis of our protocol, showing that our
protocol is computationally more efficient than existing solutions and results in a constant
communication rounds with a high probability.
48.The CoQUOS Approach to Continuous Queries in
Unstructured Overlays.
Synopsis:
The current peer-to-peer (P2P) content distribution systems are constricted by their simple
on-demand content discovery mechanism. The utility of these systems can be greatly
enhanced by incorporating two capabilities, namely a mechanism through which peers can
register their long term interests with the network so that they can be continuously notified
of new data items, and a means for the peers to advertise their contents. Although
researchers have proposed a few unstructured overlay-based publish-subscribe systems
that provide the above capabilities, most of these systems require intricate indexing and
routing schemes, which not only make them highly complex but also render the overlay
network less flexible toward transient peers. This paper argues that for many P2P
applications, implementing full-fledged publish-subscribe systems is an overkill. For these
applications, we study the alternate continuous query paradigm, which is a best-effort
service providing the above two capabilities. We present a scalable and effective
middleware, called CoQUOS, for supporting continuous queries in unstructured overlay
networks. Besides being independent of the overlay topology, CoQUOS preserves the
simplicity and flexibility of the unstructured P2P network. Our design of the CoQUOS
system is characterized by two novel techniques, namely cluster-resilient random walk
algorithm for propagating the queries to various regions of the network and dynamic
probability-based query registration scheme to ensure that the registrations are well
distributed in the overlay. Further, we also develop effective and efficient schemes for
providing resilience to the churn of the P2P network and for ensuring a fair distribution of
the notification load among the peers. This paper studies the properties of our algorithms
through theoretical analysis. We also report series of experiments evaluating the
effectiveness and the costs of the proposed schemes.
49.Mining Weakly Labeled Web Facial Images for Search-
Based Face Annotation.
Synopsis:

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This paper investigates a framework of search-based face annotation (SBFA) by mining weakly
labeled facial images that are freely available on the World Wide Web (WWW). One challenging
problem for search-based face annotation scheme is how to effectively perform annotation by
exploiting the list of most similar facial images and their weak labels that are often noisy and
incomplete. To tackle this problem, we propose an effective unsupervised label refinement
(ULR) approach for refining the labels of web facial images using machine learning techniques.
We formulate the learning problem as a convex optimization and develop effective optimization
algorithms to solve the large-scale learning task efficiently. To further speed up the proposed
scheme, we also propose a clustering-based approximation algorithm which can improve the
scalability considerably. We have conducted an extensive set of empirical studies on a large-
scale web facial image testbed, in which encouraging results showed that the proposed ULR
algorithms can significantly boost the performance of the promising SBFA scheme.
50.Comparable Entity Mining from Comparative Questions.
Synopsis:
Comparing one thing with another is a typical part of human decision making process.
However, it is not always easy to know what to compare and what are the alternatives. In
this paper, we present a novel way to automatically mine comparable entities from
comparative questions that users posted online to address this difficulty. To ensure high
precision and high recall, we develop a weakly supervised bootstrapping approach for
comparative question identification and comparable entity extraction by leveraging a large
collection of online question archive. The experimental results show our method achieves
F1-measure of 82.5 percent in comparative question identification and 83.3 percent in
comparable entity extraction. Both significantly outperform an existing state-of-the-art
method. Additionally, our ranking results show highly relevance to user's comparison intents
in web.
51.Efﬁcient Computation of Range Aggregates against
Uncertain Location Based Queries.
Synopsis:
In many applications, including location-based services, queries may not be precise. In this
paper, we study the problem of efficiently computing range aggregates in a

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multidimensional space when the query location is uncertain. Specifically, for a query point
Q whose location is uncertain and a set S of points in a multidimensional space, we want to
calculate the aggregate (e.g., count, average and sum) over the subset S' of S such that for
each p ϵ S', Q has at least probability θ within the distance γ to p. We propose novel,
efficient techniques to solve the problem following the filtering-and-verification paradigm. In
particular, two novel filtering techniques are proposed to effectively and efficiently remove
data points from verification. Our comprehensive experiments based on both real and
synthetic data demonstrate the efficiency and scalability of our techniques.
52.On Skyline Groups.
Synopsis:
We formulate and investigate the novel problem of finding the skyline k-tuple groups from
an n-tuple data set-i.e., groups of k tuples which are not dominated by any other group of
equal size, based on aggregate-based group dominance relationship. The major technical
challenge is to identify effective anti-monotonic properties for pruning the search space of
skyline groups. To this end, we first show that the anti-monotonic property in the well-known
Apriori algorithm does not hold for skyline group pruning. Then, we identify two anti-
monotonic properties with varying degrees of applicability: order-specific property which
applies to SUM, MIN, and MAX as well as weak candidate-generation property which
applies to MIN and MAX only. Experimental results on both real and synthetic data sets
verify that the proposed algorithms achieve orders of magnitude performance gain over the
baseline method.
53.Co-Occurrence-Based Diffusion for Expert Search on the
Web.
Synopsis:
Expert search has been studied in different contexts, e.g., enterprises, academic
communities. We examine a general expert search problem: searching experts on the web,
where millions of webpages and thousands of names are considered. It has mainly two
challenging issues: 1) webpages could be of varying quality and full of noises; 2) The
expertise evidences scattered in webpages are usually vague and ambiguous. We propose
to leverage the large amount of co-occurrence information to assess relevance and
reputation of a person name for a query topic. The co-occurrence structure is modeled
using a hypergraph, on which a heat diffusion based ranking algorithm is proposed. Query
keywords are regarded as heat sources, and a person name which has strong connection
with the query (i.e., frequently co-occur with query keywords and co-occur with other names
related to query keywords) will receive most of the heat, thus being ranked high.

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Experiments on the ClueWeb09 web collection show that our algorithm is effective for
retrieving experts and outperforms baseline algorithms significantly. This work would be
regarded as one step toward addressing the more general entity search problem without
sophisticated NLP techniques.
54.Efﬁcient Extended Boolean Retrieval.
Synopsis:
Extended Boolean retrieval (EBR) models were proposed nearly three decades ago, but
have had little practical impact, despite their significant advantages compared to either
ranked keyword or pure Boolean retrieval. In particular, EBR models produce meaningful
rankings; their query model allows the representation of complex concepts in an and-or
format; and they are scrutable, in that the score assigned to a document depends solely on
the content of that document, unaffected by any collection statistics or other external
factors. These characteristics make EBR models attractive in domains typified by medical
and legal searching, where the emphasis is on iterative development of reproducible
complex queries of dozens or even hundreds of terms. However, EBR is much more
computationally expensive than the alternatives. We consider the implementation of the p-
norm approach to EBR, and demonstrate that ideas used in the max-score and wand exact
optimization techniques for ranked keyword retrieval can be adapted to allow selective
bypass of documents via a low-cost screening process for this and similar retrieval models.
We also propose term-independent bounds that are able to further reduce the number of
score calculations for short, simple queries under the extended Boolean retrieval model.
Together, these methods yield an overall saving from 50 to 80 percent of the evaluation
cost on test queries drawn from biomedical search.
55.On the use of Side Information for Mining Text Data.
Synopsis:
In many text mining applications, side-information is available along with the text
documents. Such side-information may be of different kinds, such as document provenance
information, the links in the document, user-access behavior from web logs, or other non-
textual attributes which are embedded into the text document. Such attributes may contain
a tremendous amount of information for clustering purposes. However, the relative
importance of this side-information may be difficult to estimate, especially when some of the
information is noisy. In such cases, it can be risky to incorporate side-information into the
mining process, because it can either improve the quality of the representation for the
mining process, or can add noise to the process. Therefore, we need a principled way to
perform the mining process, so as to maximize the advantages from using this side
information. In this paper, we design an algorithm which combines classical partitioning

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algorithms with probabilistic models in order to create an effective clustering approach. We
then show how to extend the approach to the classification problem. We present
experimental results on a number of real data sets in order to illustrate the advantages of
using such an approach.
56. Crowdsourcing Predictors of Behavioral Outcomes.
Synopsis:
Generating models from large data sets-and determining which subsets of data to mine-is
becoming increasingly automated. However, choosing what data to collect in the first place
requires human intuition or experience, usually supplied by a domain expert. This paper
describes a new approach to machine science which demonstrates for the first time that
nondomain experts can collectively formulate features and provide values for those features
such that they are predictive of some behavioral outcome of interest. This was
accomplished by building a Web platform in which human groups interact to both respond to
questions likely to help predict a behavioral outcome and pose new questions to their peers.
This results in a dynamically growing online survey, but the result of this cooperative
behavior also leads to models that can predict the user's outcomes based on their
responses to the user-generated survey questions. Here, we describe two Web-based
experiments that instantiate this approach: The first site led to models that can predict
users' monthly electric energy consumption, and the other led to models that can predict
users' body mass index. As exponential increases in content are often observed in
successful online collaborative communities, the proposed methodology may, in the future,
lead to similar exponential rises in discovery and insight into the causal factors of behavioral
outcomes.
57.Efﬁcient Similarity Search over Encrypted Data.
Synopsis:
In recent years, due to the appealing features of cloud computing, large amount of data
have been stored in the cloud. Although cloud based services offer many advantages,
privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is
desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data
against illegal access, but it complicates some basic, yet important functionality such as the
search on the data. To achieve search over encrypted data without compromising the
privacy, considerable amount of searchable encryption schemes have been proposed in the
literature. However, almost all of them handle exact query matching but not similarity
matching, a crucial requirement for real world applications. Although some sophisticated
secure multi-party computation based cryptographic techniques are available for similarity
tests, they are computationally intensive and do not scale for large data sources. In this

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paper, we propose an efficient scheme for similarity search over encrypted data. To do so,
we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional
spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data,
we provide a rigorous security definition and prove the security of the proposed scheme
under the provided definition. In addition, we provide a real world application of the
proposed scheme and verify the theoretical results with empirical observations on a real
dataset.
58.Online Feature Selection and Its Applications.
Synopsis:
Feature selection is an important technique for data mining. Despite its importance, most
studies of feature selection are restricted to batch learning. Unlike traditional batch learning
methods, online learning represents a promising family of efficient and scalable machine
learning algorithms for large-scale applications. Most existing studies of online learning
require accessing all the attributes/features of training instances. Such a classical setting is
not always appropriate for real-world applications when data instances are of high
dimensionality or it is expensive to acquire the full set of attributes/features. To address this
limitation, we investigate the problem of online feature selection (OFS) in which an online
learner is only allowed to maintain a classifier involved only a small and fixed number of
features. The key challenge of online feature selection is how to make accurate prediction
for an instance using a small number of active features. This is in contrast to the classical
setup of online learning where all the features can be used for prediction. We attempt to
tackle this challenge by studying sparsity regularization and truncation techniques.
Specifically, this article addresses two different tasks of online feature selection: 1) learning
with full input, where an learner is allowed to access all the features to decide the subset of
active features, and 2) learning with partial input, where only a limited number of features is
allowed to be accessed for each instance by the learner. We present novel algorithms to
solve each of the two problems and give their performance analysis. We evaluate the
performance of the proposed algorithms for online feature selection on several public data
sets, and demonstrate their applications to real-world problems including image
classification in computer vision and microarray gene expression analysis in bioinformatics.
The encouraging results of our experiments validate the efficacy and efficiency of th-
proposed techniques.
59.Dynamic Personalized Recommendation on Sparse Data.
Synopsis:
Recommendation techniques are very important in the fields of E-commerce and other web-
based services. One of the main difficulties is dynamically providing high-quality

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recommendation on sparse data. In this paper, a novel dynamic personalized
recommendation algorithm is proposed, in which information contained in both ratings and
profile contents are utilized by exploring latent relations between ratings, a set of dynamic
features are designed to describe user preferences in multiple phases, and finally, a
recommendation is made by adaptively weighting the features. Experimental results on
public data sets show that the proposed algorithm has satisfying performance.
60.Enabling cross-site interactions in social networks.
Synopsis:
Social Networks is one of the major technological phenomena on the Web 2.0. Hundreds of
millions of people are posting articles, photos, and videos on their profiles and interacting
with other people, but the sharing and interaction are limited within a same social network
site. Although users can share some contents in a social network site with people outside of
the social network site using a public link of content, appropriate access control
mechanisms are still not supported. To overcome those limitations, we propose a cross-site
content sharing framework named x-mngr, allowing users to interact with others in other
social network sites, with a cross-site access control policy, which enables users to specify
policies that allow/deny access to their shared contents across social network sites. We
implemented our proposed framework through a photo sharing application that shares
user's photos between Face book and My Space based on the cross-site access control
policy. To evaluate our approach, we conducted a user study for the x-mngr framework.
61.Personalized Recommendation Combining User Interest
and Social Circle.
Synopsis:
With the advent and popularity of social network, more and more users like to share their
experiences, such as ratings, reviews, and blogs. The new factors of social network like
interpersonal influence and interest based on circles of friends bring opportunities and
challenges for recommender system (RS) to solve the cold start and sparsity problem of
datasets. Some of the social factors have been used in RS, but have not been fully
considered. In this paper, three social factors, personal interest, interpersonal interest
similarity, and interpersonal influence, fuse into a unified personalized recommendation
model based on probabilistic matrix factorization. The factor of personal interest can make
the RS recommend items to meet users' individualities, especially for experienced users.
Moreover, for cold start users, the interpersonal interest similarity and interpersonal
influence can enhance the intrinsic link among features in the latent space. We conduct a

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series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie.
Experimental results show the proposed approach outperforms the existing RS approaches.
62.Dynamic Query Forms for Database Queries.
Synopsis:
Modern scientific databases and web databases maintain large and heterogeneous data.
These real-world databases contain hundreds or even thousands of relations and attributes.
Traditional predefined query forms are not able to satisfy various ad-hoc queries from users
on those databases. This paper proposes DQF, a novel database query form interface,
which is able to dynamically generate query forms. The essence of DQF is to capture a
user's preference and rank query form components, assisting him/her in making decisions.
The generation of a query form is an iterative process and is guided by the user. At each
iteration, the system automatically generates ranking lists of form components and the user
then adds the desired form components into the query form. The ranking of form
components is based on the captured user preference. A user can also fill the query form
and submit queries to view the query result at each iteration. In this way, a query form could
be dynamically refined until the user is satisfied with the query results. We utilize the
expected F-measure for measuring the goodness of a query form. A probabilistic model is
developed for estimating the goodness of a query form in DQF. Our experimental evaluation
and user study demonstrate the effectiveness and efficiency of the system.
63.Enabling Multilevel Trust in Privacy Preserving Data
Mining.
Synopsis:
Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate
models about aggregated data without access to precise information in individual data
record. A widely studied perturbation-based PPDM approach introduces random
perturbation to individual values to preserve privacy before data are published. Previous
solutions of this approach are limited in their tacit assumption of single-level trust on data
miners. In this work, we relax this assumption and expand the scope of perturbation-based
PPDM to Multilevel Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the
less perturbed copy of the data it can access. Under this setting, a malicious data miner
may have access to differently perturbed copies of the same data through various means,

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and may combine these diverse copies to jointly infer additional information about the
original data that the data owner does not intend to release. Preventing such diversity
attacks is the key challenge of providing MLT-PPDM services. We address this challenge
by properly correlating perturbation across copies at different trust levels. We prove that our
solution is robust against diversity attacks with respect to our privacy goal. That is, for data
miners who have access to an arbitrary collection of the perturbed copies, our solution
prevent them from jointly reconstructing the original data more accurately than the best
effort using any individual copy in the collection. Our solution allows a data owner to
generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers
data owners maximum flexibility.
64.Privacy-Preserving and Content-Protecting Location
Based Queries.
Synopsis:
In this paper we present a solution to one of the location-based query problems. This
problem is defined as follows: (i) a user wants to query a database of location data, known
as Points Of Interest (POIs), and does not want to reveal his/her location to the server due
to privacy concerns; (ii) the owner of the location data, that is, the location server, does not
want to simply distribute its data to all users. The location server desires to have some
control over its data, since the data is its asset. We propose a major enhancement upon
previous solutions by introducing a two stage approach, where the first step is based on
Oblivious Transfer and the second step is based on Private Information Retrieval, to
achieve a secure solution for both parties. The solution we present is efficient and practical
in many scenarios. We implement our solution on a desktop machine and a mobile device
to assess the efficiency of our protocol. We also introduce a security model and analyse the
security in the context of our protocol. Finally, we highlight a security weakness of our
previous work and present a solution to overcome it.
65.Efficient Algorithms for Mining High Utility Itemsets
from Transactional Databases.
Synopsis:
Mining high utility itemsets from a transactional database refers to the discovery of itemsets
with high utility like profits. Although a number of relevant algorithms have been proposed in
recent years, they incur the problem of producing a large number of candidate itemsets for
high utility itemsets. Such a large number of candidate itemsets degrades the mining
performance in terms of execution time and space requirement. The situation may become
worse when the database contains lots of long transactions or long high utility itemsets. In

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this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-
Growth+, for mining high utility itemsets with a set of effective strategies for pruning
candidate itemsets. The information of high utility itemsets is maintained in a tree-based
data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be
generated efficiently with only two scans of database. The performance of UP-Growth and
UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real
and synthetic data sets. Experimental results show that the proposed algorithms, especially
UP-Growth+, not only reduce the number of candidates effectively but also outperform other
algorithms substantially in terms of runtime, especially when databases contain lots of long
transactions.
66.A Framework for Personal Mobile Commerce Pattern
Mining and Prediction.
Synopsis:
Due to a wide range of potential applications, research on mobile commerce has received a
lot of interests from both of the industry and academia. Among them, one of the active topic
areas is the mining and prediction of users' mobile commerce behaviors such as their
movements and purchase transactions. In this paper, we propose a novel framework, called
Mobile Commerce Explorer (MCE), for mining and prediction of mobile users' movements
and purchase transactions under the context of mobile commerce. The MCE framework
consists of three major components: 1) Similarity Inference Model (SIM) for measuring the
similarities among stores and items, which are two basic mobile commerce entities
considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine)
algorithm for efficient discovery of mobile users' Personal Mobile Commerce Patterns
(PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible
mobile user behaviors. To our best knowledge, this is the first work that facilitates mining
and prediction of mobile users' commerce behaviors in order to recommend stores and
items previously unknown to a user. We perform an extensive experimental evaluation by
simulation and show that our proposals produce excellent results.
67.Privacy-Preserving Enhanced Collaborative Tagging.
Synopsis:
Collaborative tagging is one of the most popular services available online, and it allows end
user to loosely classify either online or offline resources based on their feedback, expressed
in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive
information, the wide use of collaborative tagging services increases the risk of cross
referencing, thereby seriously compromising user privacy. In this paper, we make a first

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contribution toward the development of a privacy-preserving collaborative tagging service,
by showing how a specific privacy-enhancing technology, namely tag suppression, can be
used to protect end-user privacy. Moreover, we analyze how our approach can affect the
effectiveness of a policy-based collaborative tagging system that supports enhanced web
access functionalities, like content filtering and discovery, based on preferences specified
by end users.
68.Efficient Evaluation of SUM Queries over Probabilistic
Data.
Synopsis:
SUM queries are crucial for many applications that need to deal with uncertain data. In this
paper, we are interested in the queries, called ALL_SUM, that return all possible sum
values and their probabilities. In general, there is no efficient solution for the problem of
evaluating ALL_SUM queries. But, for many practical applications, where aggregate values
are small integers or real numbers with small precision, it is possible to develop efficient
solutions. In this paper, based on a recursive approach, we propose a new solution for
those applications. We implemented our solution and conducted an extensive experimental
evaluation over synthetic and real-world data sets; the results show its effectiveness.
69.Fuzzy Order-of-Magnitude Based Link Analysis for
Qualitative Alias Detection.
Synopsis:
Alias detection has been the significant subject being extensively studied for several domain
applications, especially intelligence data analysis. Many preliminary methods rely on text-
based measures, which are ineffective with false descriptions of terrorists' name, date-of-
birth, and address. This barrier may be overcome through link information presented in
relationships among objects of interests. Several numerical link-based similarity techniques
have proven effective for identifying similar objects in the Internet and publication domains.
However, as a result of exceptional cases with unduly high measure, these methods usually
generate inaccurate similarity descriptions. Yet, they are either computationally inefficient or
ineffective for alias detection with a single-property based model. This paper presents a
novel orders-of-magnitude based similarity measure that integrates multiple link properties
to refine the estimation process and derive semantic-rich similarity descriptions. The
approach is based on order-of-magnitude reasoning with which the theory of fuzzy set is
blended to provide quantitative semantics of descriptors and their unambiguous
mathematical manipulation. With such explanatory formalism, analysts can validate the
generated results and partly resolve the problem of false positives. It also allows coherent

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interpretation and communication within a decision-making group, using this computing-
with-word capability. Its performance is evaluated over a terrorism-related data set, with
further generalization over publication and email data collections.
70.Secure KNN Query Processing in Untrusted Cloud
Environments.
Synopsis:
Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access
information that is relevant to their present location. Users are interested in querying about
points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing
events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in
arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to
subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping
such information up-to-date and relevant to the users is not an easy task, so the owners of
such data sets will make the data accessible only to paying customers. Users send their
current location as the query parameter, and wish to receive as result the nearest POIs, i.e.,
nearest-neighbors (NNs). But typical data owners do not have the technical means to
support processing queries on a large scale, so they outsource data storage and querying
to a cloud service provider. Many such cloud providers exist who offer powerful storage and
computational infrastructures at low cost. However, cloud providers are not fully trusted, and
typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to
answer queries correctly, but they also collect the locations of the POIs and the subscribers
for other purposes. Leakage of POI locations can lead to privacy breaches as well as
financial losses to the data owners, for whom the POI data set is an important source of
revenue. Disclosure of user locations leads to privacy violations and may deter subscribers
from using the service altogether. In this paper, we propose a family of techniques that
allow processing of NN queries in an untrusted outsourced environment, while at the same
time protecting both the POI and querying users' positions. Our techniques rely on mutable

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order preserving encoding (mOPE), the only secure order-preserving encryption method
known to-date. W- also provide performance optimizations to decrease the computational
cost inherent to processing on encrypted data, and we consider the case of incrementally
updating data sets. We present an extensive performance evaluation of our techniques to
illustrate their viability in practice.
71.Facilitating Effective User Navigation through Website
Structure Improvement.
Synopsis:
Designing well-structured websites to facilitate effective user navigation has long been a
challenge. A primary reason is that the web developers' understanding of how a website
should be structured can be considerably different from that of the users. While various
methods have been proposed to relink webpages to improve navigability using user
navigation data, the completely reorganized new structure can be highly unpredictable, and
the cost of disorienting users after the changes remains unanalyzed. This paper addresses
how to improve a website without introducing substantial changes. Specifically, we propose
a mathematical programming model to improve the user navigation on a website while
minimizing alterations to its current structure. Results from extensive tests conducted on a
publicly available real data set indicate that our model not only significantly improves the
user navigation with very few changes, but also can be effectively solved. We have also
tested the model on large synthetic data sets to demonstrate that it scales up very well. In
addition, we define two evaluation metrics and use them to assess the performance of the
improved website using the real data set. Evaluation results confirm that the user navigation
on the improved structure is indeed greatly enhanced. More interestingly, we find that
heavily disoriented users are more likely to benefit from the improved structure than the less
disoriented users.
72.Geometric Data Perturbation for Privacy Preserving
Outsourced Data Mining.
Synopsis:
Data perturbation is a popular technique in privacy-preserving data mining. A major
challenge in data perturbation is to balance privacy protection and data utility, which are
normally considered as a pair of conflicting factors. We argue that selectively preserving the
task/model specific information in perturbation will help achieve better privacy guarantee
and better data utility. One type of such information is the multidimensional geometric
information, which is implicitly utilized by many data-mining models. To preserve this
information in data perturbation, we propose the Geometric Data Perturbation (GDP)

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method. In this paper, we describe several aspects of the GDP method. First, we show that
several types of well-known data-mining models will deliver a comparable level of model
quality over the geometrically perturbed data set as over the original data set. Second, we
discuss the intuition behind the GDP method and compare it with other multidimensional
perturbation methods such as random projection perturbation. Third, we propose a multi-
column privacy evaluation framework for evaluating the effectiveness of geometric data
perturbation with respect to different level of attacks. Finally, we use this evaluation
framework to study a few attacks to geometrically perturbed data sets. Our experimental
study also shows that geometric data perturbation can not only provide satisfactory privacy
guarantee but also preserve modeling accuracy well.
73.Secure Outsourced Attribute-Based Signatures.
Synopsis:
Attribute-based signature (ABS) enables users to sign messages over attributes without
revealing any information other than the fact that they have attested to the messages.
However, heavy computational cost is required during signing in existing work of ABS,
which grows linearly with the size of the predicate formula. As a result, this presents a
significant challenge for resource-constrained devices (such as mobile devices or RFID
tags) to perform such heavy computations independently. Aiming at tackling the challenge
above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS,
in which the computational overhead at user side is greatly reduced through outsourcing
intensive computations to an untrusted signing-cloud service provider (S-CSP).
Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the
complexity. As a result, we present two concrete OABS schemes: i) in the first OABS
scheme, the number of exponentiations involving in signing is reduced from O(d) to O(1)
(nearly three), where d is the upper bound of threshold value defined in the predicate; ii) our
second scheme is built on Herranz et al.'s construction with constant-size signatures. The
number of exponentiations in signing is reduced from O(d2) to O(d) and the communication
overhead is O(1). Security analysis demonstrates that both OABS schemes are secure in
terms of the unforgeability and attribute-signer privacy definitions specified in the proposed
security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of
OABS and show how to achieve accountability as well.
74.Fairness-aware and Privacy-Preserving Friend Matching
Protocol in Mobile Social Networks.
Synopsis:

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Mobile social networks represent a promising cyber-physical system, which connects
mobile
nodes within a local physical proximity using mobile smart phones as well as wireless
communication.
In mobile social networks, the mobile users may, however, face the risk of leaking their
personal information
and location privacy. In this paper, we first model the secure friend discovery process as a
generalized privacypreserving
interest and profile matching problem. We identify a new security threat arising from existing
secure friend discovery protocols, coined as runaway attack, which can introduce a serious
unfairness issue.
To thwart this new threat, we introduce a novel blind vector transformation technique, which
could hide
the correlation between the original vector and transformed results. Based on this, we
propose our privacy preserving
and fairness-aware interest and profile matching protocol, which allows one party to match
its
interest with the profile of another, without revealing its real interest and profile and vice
versa. The detailed
security analysis as well as real-world implementations demonstrate the effectiveness and
efficiency of the
proposed protocol.
75.How do Facebookers use Friendlists.
Synopsis:
Facebook friend lists are used to classify friends into groups and assist users in controlling
access to their information. In this paper, we study the effectiveness of Facebook friend lists
from two aspects: Friend Management and Policy Patterns by examining how users build
friend lists and to what extent they use them in their policy templates. We have collected
real Facebook profile information and photo privacy policies of 222 participants, through
their consent in our Facebook survey application posted on Mechanical Turk. Our data
analysis shows that users' customized friend lists are less frequently created and have
fewer overlaps as compared to Facebook created friend lists. Also, users do not place all of

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their friends into lists. Moreover, friends in more than one friend lists have higher values of
node betweenness and outgoing to incoming edge ratio values among all the friends of a
particular user. Last but not the least, friend list and user based exceptions are less
frequently used in policies as compared to allowing all friends, friends of friends and
everyone to view photos.
76.Security Evaluation of Pattern Classiﬁers under Attack.
Synopsis:
Pattern classification systems are commonly used in adversarial applications, like biometric
authentication, network intrusion detection, and spam filtering, in which data can be
purposely manipulated by humans to undermine their operation. As this adversarial
scenario is not taken into account by classical design methods, pattern classification
systems may exhibit vulnerabilities, whose exploitation may severely affect their
performance, and consequently limit their practical utility. Extending pattern classification
theory and design methods to adversarial settings is thus a novel and very relevant
research direction, which has not yet been pursued in a systematic way. In this paper, we
address one of the main open issues: evaluating at design phase the security of pattern
classifiers, namely, the performance degradation under potential attacks they may incur
during operation. We propose a framework for empirical evaluation of classifier security that
formalizes and generalizes the main ideas proposed in the literature, and give examples of
its use in three real applications. Reported results show that security evaluation can provide
a more complete understanding of the classifier's behavior in adversarial environments, and
lead to better design choices.
78.Investigation and Analysis of New Approach of
Intelligent Semantic Web Search Engines
Synopsis:
As we know that www is allowing peoples to share the huge information from big database
repositories. The amount of information grows billions of databases. Hence to search
particular information from these huge databases we need specialized mechanism which
helps to retrive that information efficiently. now days various types of search engines are
available which makes information retrieving is difficult. but to provide the better solution to
this proplem ,semantic web search engines are playing vital role.basically main aim of this
kind of search engines is providing the required information is small time with maximum
accuracy.

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79.Self-Adaptive Semantic Focused Crawler for Mining
Services Information Discovery.
Synopsis:
It is well recognized that the Internet has become the largest marketplace in the world, and
online advertising is very popular with numerous industries, including the traditional mining
service industry where mining service advertisements are effective carriers of mining
service information. However, service users may encounter three major issues -
heterogeneity, ubiquity, and ambiguity, when searching for mining service information over
the Internet. In this paper, we present the framework of a novel self-adaptive semantic
focused crawler - SASF crawler, with the purpose of precisely and efficiently discovering,
formatting, and indexing mining service information over the Internet, by taking into account
the three major issues. This framework incorporates the technologies of semantic focused
crawling and ontology learning, in order to maintain the performance of this crawler,
regardless of the variety in the Web environment. The innovations of this research lie in the
design of an unsupervised framework for vocabulary-based ontology learning, and a hybrid
algorithm for matching semantically relevant concepts and metadata. A series of
experiments are conducted in order to evaluate the performance of this crawler. The
conclusion and the direction of future work are given in the final section.
80.FoCUS: Learning to Crawl Web Forums.
Synopsis:
In this paper, we present Forum Crawler Under Supervision (FoCUS), a supervised web-
scale forum crawler. The goal of FoCUS is to crawl relevant forum content from the web
with minimal overhead. Forum threads contain information content that is the target of forum
crawlers. Although forums have different layouts or styles and are powered by different
forum software packages, they always have similar implicit navigation paths connected by
specific URL types to lead users from entry pages to thread pages. Based on this
observation, we reduce the web forum crawling problem to a URL-type recognition problem.
And we show how to learn accurate and effective regular expression patterns of implicit
navigation paths from automatically created training sets using aggregated results from
weak page type classifiers. Robust page type classifiers can be trained from as few as five
annotated forums and applied to a large set of unseen forums. Our test results show that
FoCUS achieved over 98 percent effectiveness and 97 percent coverage on a large set of
test forums powered by over 150 different forum software packages. In addition, the results
of applying FoCUS on more than 100 community Question and Answer sites and Blog sites
demonstrated that the concept of implicit navigation path could apply to other social media
sites.

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81.Efﬁcient Multi-dimensional Fuzzy Search for Personal
Information Management Systems.
Synopsis:
With the explosion in the amount of semistructured data users access and store in personal
information management systems, there is a critical need for powerful search tools to
retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically
support some IR-style ranking on the textual part of the query, but only consider structure
(e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a
novel multidimensional search approach that allows users to perform fuzzy searches for
structure and metadata conditions in addition to keyword conditions. Our techniques
individually score each dimension and integrate the three dimension scores into a
meaningful unified score. We also design indexes and algorithms to efficiently identify the
most relevant files that match multidimensional queries. We perform a thorough
experimental evaluation of our approach and show that our relaxation and scoring
framework for fuzzy query conditions in noncontent dimensions can significantly improve
ranking accuracy. We also show that our query processing strategies perform and scale
well, making our fuzzy search approach practical for every day usage.
82.Supporting Privacy Protection in Personalized Web
Search.
Synopsis:
Personalized web search (PWS) has demonstrated its effectiveness in improving the quality
of various search services on the Internet. However, evidences show that users' reluctance
to disclose their private information during search has become a major barrier for the wide
proliferation of PWS. We study privacy protection in PWS applications that model user
preferences as hierarchical user profiles. We propose a PWS framework called UPS that
can adaptively generalize profiles by queries while respecting user-specified privacy
requirements. Our runtime generalization aims at striking a balance between two predictive
metrics that evaluate the utility of personalization and the privacy risk of exposing the
generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for
runtime generalization. We also provide an online prediction mechanism for deciding
whether personalizing a query is beneficial. Extensive experiments demonstrate the
effectiveness of our framework. The experimental results also reveal that GreedyIL
significantly outperforms GreedyDP in terms of efficiency.

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83.Improving Security and Efficiency in Attribute-Based
Data Sharing.
Synopsis:
With the recent adoption and diffusion of the data sharing paradigm in distributed systems
such as online social networks or cloud computing, there have been increasing demands
and concerns for distributed data security. One of the most challenging issues in data
sharing systems is the enforcement of access policies and the support of policies updates.
Ciphertext policy attribute-based encryption (CP-ABE) is becoming a promising
cryptographic solution to this issue. It enables data owners to define their own access
policies over user attributes and enforce the policies on the data to be distributed. However,
the advantage comes with a major drawback which is known as a key escrow problem. The
key generation center could decrypt any messages addressed to specific users by
generating their private keys. This is not suitable for data sharing scenarios where the data
owner would like to make their private data only accessible to designated users. In addition,
applying CP-ABE in the data sharing system introduces another challenge with regard to
the user revocation since the access policies are defined only over the attribute universe.
Therefore, in this study, we propose a novel CP-ABE scheme for a data sharing system by
exploiting the characteristic of the system architecture. The proposed scheme features the
following achievements: 1) the key escrow problem could be solved by escrow-free key
issuing protocol, which is constructed using the secure two-party computation between the
key generation center and the data-storing center, and 2) fine-grained user revocation per
each attribute could be done by proxy encryption which takes advantage of the selective
attribute group key distribution on top of the ABE. The performance and security analyses
indicate that the proposed scheme is efficient to securely manage the data distributed in the
data sharing system.
84.Multiparty Access Control for Online Social Networks:
Model and Mechanisms..
Synopsis:
Online social networks (OSNs) have experienced tremendous growth in recent years and
become a de facto portal for hundreds of millions of Internet users. These OSNs offer
attractive means for digital social interactions and information sharing, but also raise a
number of security and privacy issues. While OSNs allow users to restrict access to shared
data, they currently do not provide any mechanism to enforce privacy concerns over data
associated with multiple users. To this end, we propose an approach to enable the
protection of shared data associated with multiple users in OSNs. We formulate an access
control model to capture the essence of multiparty authorization requirements, along with a

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multiparty policy specification scheme and a policy enforcement mechanism. Besides, we
present a logical representation of our access control model that allows us to leverage the
features of existing logic solvers to perform various analysis tasks on our model. We also
discuss a proof-of-concept prototype of our approach as part of an application in Facebook
and provide usability study and system evaluation of our method.
85.Task Trail An Effective Segmentation of User Search
Behavior.
Synopsis:
In this paper, we introduce “task trail” to understand user search behaviors. We define a
task to be an atomic user information need, whereas a task trail represents all user activities
within that particular task, such as query reformulations, URL clicks. Previously, web search
logs have been studied mainly at session or query level where users may submit several
queries within one task and handle several tasks within one session. Although previous
studies have addressed the problem of task identification, little is known about the
advantage of using task over session or query for search applications. In this paper, we
conduct extensive analyses and comparisons to evaluate the effectiveness of task trails in
several search applications: determining user satisfaction, predicting user search interests,
and suggesting related queries. Experiments on large scale data sets of a commercial
search engine show that: (1) Task trail performs better than session and query trails in
determining user satisfaction; (2) Task trail increases webpage utilities of end users
comparing to session and query trails; (3) Task trails are comparable to query trails but
more sensitive than session trails in measuring different ranking functions; (4) Query terms
from the same task are more topically consistent to each other than query terms from
different tasks; (5) Query suggestion based on task trail is a good complement of query
suggestions based on session trail and click-through bipartite. The findings in this paper
verify the need of extracting task trails from web search logs and enhance applications in
search and recommendation systems.
86.Incentive Compatible Privacy-Preserving Data Analysis.
Synopsis:
In many cases, competing parties who have private data may collaboratively conduct
privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or
analysis results. Most often, the competing parties have different incentives. Although
certain PPDA techniques guarantee that nothing other than the final analysis result is
revealed, it is impossible to verify whether participating parties are truthful about their
private input data. Unless proper incentives are set, current PPDA techniques cannot

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prevent participating parties from modifying their private inputs.incentive compatible privacy-
preserving data analysis techniques This raises the question of how to design incentive
compatible privacy-preserving data analysis techniques that motivate participating parties to
provide truthful inputs. In this paper, we first develop key theorems, then base on these
theorems, we analyze certain important privacy-preserving data analysis tasks that could be
conducted in a way that telling the truth is the best choice for any participating party.
87.On the Spectral Characterization and Scalable Mining of
Network Communities.
Synopsis:
Network communities refer to groups of vertices within which their connecting links are
dense but between which they are sparse. A network community mining problem (or NCMP
for short) is concerned with the problem of finding all such communities from a given
network. A wide variety of applications can be formulated as NCMPs, ranging from social
and/or biological network analysis to web mining and searching. So far, many algorithms
addressing NCMPs have been developed and most of them fall into the categories of either
optimization based or heuristic methods. Distinct from the existing studies, the work
presented in this paper explores the notion of network communities and their properties
based on the dynamics of a stochastic model naturally introduced. In the paper, a
relationship between the hierarchical community structure of a network and the local mixing
properties of such a stochastic model has been established with the large-deviation theory.
Topological information regarding to the community structures hidden in networks can be
inferred from their spectral signatures. Based on the above-mentioned relationship, this
work proposes a general framework for characterizing, analyzing, and mining network
communities. Utilizing the two basic properties of metastability, i.e., being locally uniform
and temporarily fixed, an efficient implementation of the framework, called the LM algorithm,
has been developed that can scalably mine communities hidden in large-scale networks.
The effectiveness and efficiency of the LM algorithm have been theoretically analyzed as
well as experimentally validated.
88.Towards Differential Query Services in Cost-Efﬁcient
Clouds.
Synopsis:
Cloud computing as an emerging technology trend is expected to reshape the advances in
information technology. In a cost-efficient cloud environment, a user can tolerate a certain
degree of delay while retrieving information from the cloud to reduce costs. In this paper, we
address two fundamental issues in such an environment: privacy and efficiency. We first

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review a private keyword-based file retrieval scheme that was originally proposed by
Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server
without leaking any information. The main drawback is that it will cause a heavy querying
overhead incurred on the cloud and thus goes against the original intention of cost
efficiency. In this paper, we present three efficient information retrieval for ranked query
(EIRQ) schemes to reduce querying overhead incurred on the cloud. In EIRQ, queries are
classified into multiple ranks, where a higher ranked query can retrieve a higher percentage
of matched files. A user can retrieve files on demand by choosing queries of different ranks.
This feature is useful when there are a large number of matched files, but the user only
needs a small subset of them. Under different parameter settings, extensive evaluations
have been conducted on both analytical models and on a real cloud environment, in order
to examine the effectiveness of our schemes.
89.Learning Regularized, Query-dependent Bilinear
Similarities for Large Scale Image Retrieval.
Synopsis:
An effective way to improve the quality of image retrieval is by employing a query-
dependent similarity measure. However, implementing this in a large scale system is non-
trivial because we want neither hurting the efficiency nor relying on too many training
samples. In this paper, we introduce a query-dependent bilinear similarity measure to
address the first issue. Based on our bilinear similarity model, query adaptation can be
achieved by simply applying any existing efficient indexing/retrieval method to a
transformed version (surrogate) of a query. To address the issue of limited training samples,
we further propose a novel angular regularization constraint for learning the similarity
measure. The learning is formulated as a Quadratic Programming (QP) problem and can be
solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-
million web-image dataset validate that our proposed method can consistently bring
improvements and the whole solution is practical in large scale applications.
90.Organizing User Search Histories.
Synopsis:
Users are increasingly pursuing complex task-oriented goals on the web, such as making
travel arrangements, managing finances, or planning purchases. To this end, they usually
break down the tasks into a few codependent steps and issue multiple queries around these
steps repeatedly over long periods of time. To better support users in their long-term
information quests on the web, search engines keep track of their queries and clicks while
searching online. In this paper, we study the problem of organizing a user's historical

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queries into groups in a dynamic and automated fashion. Automatically identifying query
groups is helpful for a number of different search engine components and applications, such
as query suggestions, result ranking, query alterations, sessionization, and collaborative
search. In our approach, we go beyond approaches that rely on textual similarity or time
thresholds, and we propose a more robust approach that leverages search query logs. We
experimentally study the performance of different techniques, and showcase their potential,
especially when combined together.
91.XSPath: Navigation on XML Schemas Made Easy.
Synopsis:
Schemas are often used to constrain the content and structure of XML documents. They
can be quite big and complex and, thus, difficult to be accessed manually. The ability to
query a single schema, a collection of schemas or to retrieve schema components that
meet certain structural constraints significantly eases schema management and is, thus,
useful in many contexts. In this paper, we propose a query language, named XSPath,
specifically tailored for XML schema that works on logical graph-based representations of
schemas, on which it enables the navigation, and allows the selection of nodes. We also
propose XPath/XQuery-based translations that can be exploited for the evaluation of
XSPath queries. An extensive evaluation of the usability and efficiency of the proposed
approach is finally presented within the EXup system.
92.Mining User Queries with Markov Chains: Application
to Online Image Retrieval..
Synopsis:
We propose a novel method for automatic annotation, indexing and annotation-based
retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is
presented in the context of an online image retrieval system. Assuming such a system, the
users' queries are used to construct an Aggregate Markov Chain (AMC) through which the
relevance between the keywords seen by the system is defined. The users' queries are also
used to automatically annotate the images. A stochastic distance between images, based
on their annotation and the keyword relevance captured in the AMC, is then introduced.
Geometric interpretations of the proposed distance are provided and its relation to a
clustering in the keyword space is investigated. By means of a new measure of Markovian
state similarity, the mean first cross passage time (CPT), optimality properties of the
proposed distance are proved. Images are modeled as points in a vector space and their
similarity is measured with MSI. The new method is shown to possess certain theoretical
advantages and also to achieve better Precision versus Recall results when compared to

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Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods
in Annotation-Based Image Retrieval (ABIR) tasks.
93.Ranking Model Adaptation for Domain-Speciﬁc Search.
Synopsis:
With the explosive emergence of vertical search domains, applying the broad-based ranking
model directly to different domains is no longer desirable due to domain differences, while
building a unique ranking model for each domain is both laborious for labeling data and time
consuming for training models. In this paper, we address these difficulties by proposing a
regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we
can adapt an existing ranking model to a new domain, so that the amount of labeled data
and the training cost is reduced while the performance is still guaranteed. Our algorithm
only requires the prediction from the existing ranking models, rather than their internal
representations or the data from auxiliary domains. In addition, we assume that documents
similar in the domain-specific feature space should have consistent rankings, and add some
constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking
adaptability measurement is proposed to quantitatively estimate if an existing ranking model
can be adapted to a new domain. Experiments performed over Letor and two large scale
data sets crawled from a commercial search engine demonstrate the applicabilities of the
proposed ranking adaptation algorithms and the ranking adaptability measurement.
94.m-Privacy for Collaborative Data Publishing.
Synopsis:

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94.Secure Outsourced Attribute-Based Signatures.
Synopsis:
95.Resilient Identity Crime Detection.
Synopsis:
Identity crime is well known, prevalent, and costly; and credit application fraud is a specific
case of identity crime. The existing nondata mining detection system of business rules and
scorecards, and known fraud matching have limitations. To address these limitations and
combat identity crime in real time, this paper proposes a new multilayered detection system
complemented with two additional layers: communal detection (CD) and spike detection
(SD). CD finds real social relationships to reduce the suspicion score, and is tamper
resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set
of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-
resistant for attributes. It is the attribute-oriented approach on a variable-size set of
attributes. Together, CD and SD can detect more types of attacks, better account for
changing legal behavior, and remove the redundant attributes. Experiments were carried
out on CD and SD with several million real credit applications. Results on the data support
the hypothesis that successful credit application fraud patterns are sudden and exhibit
sharp spikes in duplicates. Although this research is specific to credit application fraud
detection, the concept of resilience, together with adaptivity and quality data discussed in
the paper, are general to the design, implementation, and evaluation of all detection
systems.

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96.Online Search and Buying Behaviour in Consumer
Markets.
Synopsis:
Online search behaviour is analysed using a novel methodology based on an international
panel of two million users. Consumer search is measured by the size and distribution of
online consideration sets and the use of price comparison engines in a range of US and UK
consumer markets. It is shown that most online researchers who are considering competing
suppliers only view two or three competitor websites, which results in an average online
consideration set of between 2.1 and 2.8, regardless of the use of price comparison
websites. Consumer perceived risk is negatively correlated with the size of online
consideration sets and online price competition intensity. Using international data from
fifteen countries it is shown that online research and online purchasing are negatively
correlated with shop density. The implications for managers are outlined, in particular the
importance of branding and advertising to improve the likelihood of inclusion in online
consideration sets.
97.Sequential Anomaly Detection in the Presence of Noise
and Limited Feedback.
Synopsis:
This paper describes a methodology for detecting anomalies from sequentially observed
and potentially noisy data. The proposed approach consists of two main elements: 1)
filtering, or assigning a belief or likelihood to each successive measurement based upon our
ability to predict it from previous noisy observations and 2) hedging, or flagging potential
anomalies by comparing the current belief against a time-varying and data-adaptive
threshold. The threshold is adjusted based on the available feedback from an end user. Our
algorithms, which combine universal prediction with recent work on online convex
programming, do not require computing posterior distributions given all current observations
and involve simple primal-dual parameter updates. At the heart of the proposed approach
lie exponential-family models which can be used in a wide variety of contexts and
applications, and which yield methods that achieve sublinear per-round regret against both
static and slowly varying product distributions with marginals drawn from the same
exponential family. Moreover, the regret against static distributions coincides with the
minimax value of the corresponding online strongly convex game. We also prove bounds on
the number of mistakes made during the hedging step relative to the best offline choice of
the threshold with access to all estimated beliefs and feedback signals. We validate the
theory on synthetic data drawn from a time-varying distribution over binary vectors of high
dimensionality, as well as on the Enron email dataset.

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98.Optimal Client-Server Assignment for Internet
Distributed Systems.
Synopsis:
We investigate an underlying mathematical model and algorithms for optimizing the
performance of a class of distributed systems over the Internet. Such a system consists of a
large number of clients who communicate with each other indirectly via a number of
intermediate servers. Optimizing the overall performance of such a system then can be
formulated as a client-server assignment problem whose aim is to assign the clients to the
servers in such a way to satisfy some prespecified requirements on the communication cost
and load balancing. We show that 1) the total communication load and load balancing are
two opposing metrics, and consequently, their tradeoff is inherent in this class of distributed
systems; 2) in general, finding the optimal client-server assignment for some prespecified
requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose
a heuristic via relaxed convex optimization for finding the approximate solution. Our
simulation results indicate that the proposed algorithm produces superior performance than
other heuristics, including the popular Normalized Cuts algorithm.
99.Slicing: A New Approach to Privacy Preserving Data
Publishing.
Synopsis:
Several anonymization techniques, such as generalization and bucketization, have been
designed for privacy preserving microdata publishing. Recent work has shown that
generalization loses considerable amount of information, especially for high-dimensional
data. Bucketization, on the other hand, does not prevent membership disclosure and does
not apply for data that do not have a clear separation between quasi-identifying attributes
and sensitive attributes. In this paper, we present a novel technique called slicing, which
partitions the data both horizontally and vertically. We show that slicing preserves better
data utility than generalization and can be used for membership disclosure protection.
Another important advantage of slicing is that it can handle high-dimensional data. We show
how slicing can be used for attribute disclosure protection and develop an efficient algorithm
for computing the sliced data that obey the ℓ-diversity requirement. Our workload
experiments confirm that slicing preserves better utility than generalization and is more
effective than bucketization in workloads involving the sensitive attribute. Our experiments
also demonstrate that slicing can be used to prevent membership disclosure.

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100.Personalized QoS-Aware Web Service Recommendation
and Visualization.
Synopsis:
With the proliferation of web services, effective QoS-based approach to service
recommendation is becoming more and more important. Although service recommendation
has been studied in the recent literature, the performance of existing ones is not
satisfactory, since (1) previous approaches fail to consider the QoS variance according to
users' locations; and (2) previous recommender systems are all black boxes providing
limited information on the performance of the service candidates. In this paper, we propose
a novel collaborative filtering algorithm designed for large-scale web service
recommendation. Different from previous work, our approach employs the characteristic of
QoS and achieves considerable improvement on the recommendation accuracy. To help
service users better understand the rationale of the recommendation and remove some of
the mystery, we use a recommendation visualization technique to show how a
recommendation is grouped with other choices. Comprehensive experiments are conducted
using more than 1.5 million QoS records of real-world web service invocations. The
experimental results show the efficiency and effectiveness of our approach.
101.Statistical Entity Extraction from Web .
Synopsis:
There are various kinds of valuable semantic information about real-world entities
embedded in webpages and databases. Extracting and integrating these entity information
from the Web is of great significance. Comparing to traditional information extraction
problems, web entity extraction needs to solve several new challenges to fully take
advantage of the unique characteristic of the Web. In this paper, we introduce our recent
work on statistical extraction of structured entities, named entities, entity facts and relations
from Web. We also briefly introduce iKnoweb, an interactive knowledge mining framework
for entity information integration. We will use two novel web applications, Microsoft
Academic Search (aka Libra) and EntityCube, as working examples.
102.PMSE -A Personalized Mobile Search Engine.
Synopsis:
We propose a personalized mobile search engine (PMSE) that captures the users'
preferences in the form of concepts by mining their clickthrough data. Due to the importance
of location information in mobile search, PMSE classifies these concepts into content

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concepts and location concepts. In addition, users' locations (positioned by GPS) are used
to supplement the location concepts in PMSE. The user preferences are organized in an
ontology-based, multifacet user profile, which are used to adapt a personalized ranking
function for rank adaptation of future search results. To characterize the diversity of the
concepts associated with a query and their relevances to the user's need, four entropies are
introduced to balance the weights between the content and location facets. Based on the
client-server model, we also present a detailed architecture and design for implementation
of PMSE. In our design, the client collects and stores locally the clickthrough data to protect
privacy, whereas heavy tasks such as concept extraction, training, and reranking are
performed at the PMSE server. Moreover, we address the privacy issue by restricting the
information in the user profile exposed to the PMSE server with two privacy parameters. We
prototype PMSE on the Google Android platform. Experimental results show that PMSE
significantly improves the precision comparing to the baseline.
103.Toward Private Joins on Outsourced Data..
Synopsis:
In an outsourced database framework, clients place data management responsibilities with
specialized service providers. Of essential concern in such frameworks is data privacy.
Potential clients are reluctant to outsource sensitive data to a foreign party without strong
privacy assurances beyond policy “fine prints.” In this paper, we introduce
a mechanism for executing general binary JOIN operations (for predicates that satisfy
certain properties) in an outsourced relational database framework with computational
privacy and low overhead—the first, to the best of our knowledge. We illustrate via a
set of relevant instances of JOIN predicates, including: range and equality (e.g., for
geographical data), Hamming distance (e.g., for DNA matching), and semantics (i.e., in
health-care scenarios—mapping antibiotics to bacteria). We experimentally evaluate
the main overhead components and show they are reasonable. The initial client
computation overhead for 100,000 data items is around 5 minutes and our privacy
mechanisms can sustain theoretical throughputs of several million predicate evaluations per
second, even for an unoptimized OpenSSL-based implementation.
104.Preventing Private Information Inference Attacks on
Social Networks.
Synopsis:
Online social networks, such as Facebook, are increasingly utilized by many people. These
networks allow users to publish details about themselves and to connect to their friends.
Some of the information revealed inside these networks is meant to be private. Yet it is

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possible to use learning algorithms on released data to predict private information. In this
paper, we explore how to launch inference attacks using released social networking data to
predict private information. We then devise three possible sanitization techniques that could
be used in various situations. Then, we explore the effectiveness of these techniques and
attempt to use methods of collective inference to discover sensitive attributes of the data
set. We show that we can decrease the effectiveness of both local and relational
classification algorithms by using the sanitization methods we described.
105.Privacy against Aggregate Knowledge Attacks.
Synopsis:
This paper focuses on protecting the privacy of individuals in publication scenarios where
the attacker is expected to have only abstract or aggregate knowledge about each record.
Whereas, data privacy research usually focuses on defining stricter privacy guarantees that
assume increasingly more sophisticated attack scenarios, it is also important to have
anonymization methods and guarantees that will address any attack scenario. Enforcing a
stricter guarantee than required increases unnecessarily the information loss. Consider for
example the publication of tax records, where attackers might only know the total income,
and not its constituent parts. Traditional anonymization methods would protect user privacy
by creating equivalence classes of identical records. Alternatively, in this work we propose
an anonymization technique that generalizes attributes, only as much as needed to
guarantee that aggregate values over the complete record, will create equivalence classes
of at size k. The experimental evaluation on real data shows that the proposed method
produces anonymized data that lie closer to the original ones, with respect to traditional
anonymization algorithms.
106.Privacy-preserving Mining of Association Rules from
Outsourced Transaction Databases.
Synopsis:
Spurred by developments such as cloud computing, there has been considerable recent
interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in
expertise or computational resources can outsource its mining needs to a third party service
provider (server). However, both the items and the association rules of the outsourced
database are considered private property of the corporation (data owner). To protect
corporate privacy, the data owner transforms its data and ships it to the server, sends
mining queries to the server, and recovers the true patterns from the extracted patterns
received from the server. In this paper, we study the problem of outsourcing the association
rule mining task within a corporate privacy-preserving framework. We propose an attack

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model based on background knowledge and devise a scheme for privacy preserving
outsourced mining. Our scheme ensures that each transformed item is indistinguishable
with respect to the attacker's background knowledge, from at least k-1 other transformed
items. Our comprehensive experiments on a very large and real transaction database
demonstrate that our techniques are effective, scalable, and protect privacy.
107.Ranking on Data Manifold with Sink Points.
Synopsis:
Ranking is an important problem in various applications, such as Information Retrieval (IR),
natural language processing, computational biology, and social sciences. Many ranking
approaches have been proposed to rank objects according to their degrees of relevance or
importance. Beyond these two goals, diversity has also been recognized as a crucial
criterion in ranking. Top ranked results are expected to convey as little redundant
information as possible, and cover as many aspects as possible. However, existing ranking
approaches either take no account of diversity, or handle it separately with some heuristics.
In this paper, we introduce a novel approach, Manifold Ranking with Sink Points (MRSPs),
to address diversity as well as relevance and importance in ranking. Specifically, our
approach uses a manifold ranking process over the data manifold, which can naturally find
the most relevant and important data objects. Meanwhile, by turning ranked objects into
sink points on data manifold, we can effectively prevent redundant objects from receiving a
high rank. MRSP not only shows a nice convergence property, but also has an interesting
and satisfying optimization explanation. We applied MRSP on two application tasks, update
summarization and query recommendation, where diversity is of great concern in ranking.
Experimental results on both tasks present a strong empirical performance of MRSP as
compared to existing ranking approaches.
108.Robust Module-based Data Management.
Synopsis:
The current trend for building an ontology-based data management system (DMS) is to
capitalize on efforts made to design a preexisting well-established DMS (a reference
system). The method amounts to extracting from the reference DMS a piece of schema
relevant to the new application needs-a module-, possibly personalizing it with extra
constraints w.r.t. the application under construction, and then managing a data set using the
resulting schema. In this paper, we extend the existing definitions of modules and we
introduce novel properties of robustness that provide means for checking easily that a
robust module-based DMS evolves safely w.r.t. both the schema and the data of the
reference DMS. We carry out our investigations in the setting of description logics which
underlie modern ontology languages, like RDFS, OWL, and OWL2 from W3C. Notably, we

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focus on the DL-liteA dialect of the DL-lite family, which encompasses the foundations of
the QL profile of OWL2 (i.e., DL-liteR): the W3C recommendation for efficiently managing
large data sets.
109.Secure Mining of Association Rules in Horizontally
Distributed Databases.
Synopsis:
We propose a protocol for secure mining of association rules in horizontally distributed
databases. The current leading protocol is that of Kantarcioglu and Clifton . Our protocol,
like theirs, is based on the Fast Distributed Mining (FDM)algorithm of Cheung et al. , which
is an unsecured distributed version of the Apriori algorithm. The main ingredients in our
protocol are two novel secure multi-party algorithms-one that computes the union of private
subsets that each of the interacting players hold, and another that tests the inclusion of an
element held by one player in a subset held by another. Our protocol offers enhanced
privacy with respect to the protocol in . In addition, it is simpler and is significantly more
efficient in terms of communication rounds, communication cost and computational cost.
110.Sensitive Label Privacy Protection on Social Network
Data.
Synopsis:
Privacy is one of the major concerns when publishing or sharing social network data for
social science research and business analysis. Recently, researchers have developed
privacy models similar to k-anonymity to prevent node reidentification through structure
information. However, even when these privacy models are enforced, an attacker may still
be able to infer one's private information if a group of nodes largely share the same
sensitive labels (i.e., attributes). In other words, the label-node relationship is not well
protected by pure structure anonymization methods. Furthermore, existing approaches,
which rely on edge editing or node clustering, may significantly alter key graph properties. In
this paper, we define a k-degree-l-diversity anonymity model that considers the protection of
structural information as well as sensitive labels of individuals. We further propose a novel
anonymization methodology based on adding noise nodes. We develop a new algorithm by
adding noise nodes into the original graph with the consideration of introducing the least
distortion to graph properties. Most importantly, we provide a rigorous analysis of the
theoretical bounds on the number of noise nodes added and their impacts on an important
graph property. We conduct extensive experiments to evaluate the effectiveness of the
proposed technique.

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111.Spatial Approximate String Search.
Synopsis:
This work deals with the approximate string search in large spatial databases. Specifically,
we investigate range queries augmented with a string similarity search predicate in both
euclidean space and road networks. We dub this query the spatial approximate string (SAS)
query. In euclidean space, we propose an approximate solution, the MHR-tree, which
embeds min-wise signatures into an R-tree. The min-wise signature for an index node u
keeps a concise representation of the union of q-grams from strings under the subtree of u.
We analyze the pruning functionality of such signatures based on the set resemblance
between the query string and the q-grams from the subtrees of index nodes. We also
discuss how to estimate the selectivity of a SAS query in euclidean space, for which we
present a novel adaptive algorithm to find balanced partitions using both the spatial and
string information stored in the tree. For queries on road networks, we propose a novel
exact method, RSASSOL, which significantly outperforms the baseline algorithm in practice.
The RSASSOL combines the q-gram-based inverted lists and the reference nodes based
pruning. Extensive experiments on large real data sets demonstrate the efficiency and
effectiveness of our approaches.
112.Spatial Query Integrity with Voronoi Neighbors.
Synopsis:
With the popularity of location-based services and the abundant usage of smart phones and
GPS-enabled devices, the necessity of outsourcing spatial data has grown rapidly over the
past few years. Meanwhile, the fast arising trend of cloud storage and cloud computing
services has provided a flexible and cost-effective platform for hosting data from businesses
and individuals, further enabling many location-based applications. Nevertheless, in this
database outsourcing paradigm, the authentication of the query results at the client remains
a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB)
model and propose an efficient scheme, called VN-Auth, which allows a client to verify the
correctness and completeness of the result set. Our approach is based on neighborhood
information derived from the Voronoi diagram of the underlying spatial data set and can
handle fundamental spatial query types, such as k nearest neighbor and range queries, as
well as more advanced query types like reverse k nearest neighbor, aggregate nearest
neighbor, and spatial skyline. We evaluated VN-Auth based on real-world data sets using
mobile devices (Google Droid smart phones with Android OS) as query clients. Compared
to the current state-of-the-art approaches (i.e., methods based on Merkle Hash Trees), our
experiments show that VN-Auth produces significantly smaller verification objects and is
more computationally efficient, especially for queries with low selectivity.

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113.SybilDefender Defend Against Sybil Attacks in Large
Social Networks.
Synopsis:
Distributed systems without trusted identities are particularly vulnerable to sybil attacks,
where an adversary creates multiple bogus identities to compromise the running of the
system. This paper presents SybilDefender, a sybil defense mechanism that leverages the
network topologies to defend against sybil attacks in social networks. Based on performing
a limited number of random walks within the social graphs, SybilDefender is efficient and
scalable to large social networks. Our experiments on two 3,000,000 node real-world social
topologies show that SybilDefender outperforms the state of the art by one to two orders of
magnitude in both accuracy and running time. SybilDefender can effectively identify the
sybil nodes and detect the sybil community around a sybil node, even when the number of
sybil nodes introduced by each attack edge is close to the theoretically detectable lower
bound. Besides, we propose two approaches to limiting the number of attack edges in
online social networks. The survey results of our Facebook application show that the
assumption made by previous work that all the relationships in social networks are trusted
does not apply to online social networks, and it is feasible to limit the number of attack
edges in online social networks by relationship rating.
114.User Action Interpretation for Online Content
Optimization.
Synopsis:
Web portal services have become an important medium to deliver digital content and
service, such as news, advertisements, and so on, to Web users in a timely fashion. To
attract more users to various content modules on the Web portal, it is necessary to design a
recommender system that can effectively achieve online content optimization by
automatically estimating content items' attractiveness and relevance to users' interests.
User interaction plays a vital role in building effective content optimization, as both implicit
user feedbacks and explicit user ratings on the recommended items form the basis for
designing and learning recommendation models. However, user actions on real-world Web
portal services are likely to represent many implicit signals about users' interests and
content attractiveness, which need more accurate interpretation to be fully leveraged in the
recommendation models. To address this challenge, we investigate a couple of critical
aspects of the online learning framework for personalized content optimization on Web
portal services, and, in this paper, we propose deeper user action interpretation to enhance
those critical aspects. In particular, we first propose an approach to leverage historical user
activity to build behavior-driven user segmentation; then, we introduce an approach for

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interpreting users' actions from the factors of both user engagement and position bias to
achieve unbiased estimation of content attractiveness. Our experiments on the large-scale
data from a commercial Web recommender system demonstrate that recommendation
models with our user action interpretation can reach significant improvement in terms of
online content optimization over the baseline method. The effectiveness of our user action
interpretation is also proved by the online test results on real user traffic.
115.A Cocktail Approach for Travel Package
Recommendation.
Synopsis:
Recent years have witnessed an increased interest in recommender systems. Despite
significant progress in this field, there still remain numerous avenues to explore. Indeed, this
paper provides a study of exploiting online travel information for personalized travel
package recommendation. A critical challenge along this line is to address the unique
characteristics of travel data, which distinguish travel packages from traditional items for
recommendation. To that end, in this paper, we first analyze the characteristics of the
existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST
model can represent travel packages and tourists by different topic distributions, where the
topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations,
travel seasons) of the landscapes. Then, based on this topic model representation, we
propose a cocktail approach to generate the lists for personalized travel package
recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-
season topic (TRAST) model for capturing the latent relationships among the tourists in
each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail
recommendation approach on the real-world travel package data. Experimental results
show that the TAST model can effectively capture the unique characteristics of the travel
data and the cocktail approach is, thus, much more effective than traditional
recommendation techniques for travel package recommendation. Also, by considering
tourist relationships, the TRAST model can be used as an effective assessment for travel
group formation.
116.A Decentralized Privacy Preserving Reputation
Protocol for the Malicious Adversarial Model.
Synopsis:
Users hesitate to submit negative feedback in reputation systems due to the fear of
retaliation from the recipient user. A privacy preserving reputation protocol protects users by
hiding their individual feedback and revealing only the reputation score. We present a

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privacy preserving reputation protocol for the malicious adversarial model. The malicious
users in this model actively attempt to learn the private feedback values of honest users as
well as to disrupt the protocol. Our protocol does not require centralized entities, trusted
third parties, or specialized platforms, such as anonymous networks and trusted hardware.
Moreover, our protocol is efficient. It requires an exchange of messages, where and are the
number of users in the protocol and the environment, respectively.
117.A Query Formulation Language for the data web.
Synopsis:
We present a query formulation language (called MashQL) in order to easily query and fuse
structured data on the web. The main novelty of MashQL is that it allows people with limited
IT skills to explore and query one (or multiple) data sources without prior knowledge about
the schema, structure, vocabulary, or any technical details of these sources. More
importantly, to be robust and cover most cases in practice, we do not assume that a data
source should have - an offline or inline - schema. This poses several language-design and
performance complexities that we fundamentally tackle. To illustrate the query formulation
power of MashQL, and without loss of generality, we chose the Data web scenario. We also
chose querying RDF, as it is the most primitive data model; hence, MashQL can be similarly
used for querying relational databases and XML. We present two implementations of
MashQL, an online mashup editor, and a Firefox add on. The former illustrates how
MashQL can be used to query and mash up the Data web as simple as filtering and piping
web feeds; and the Firefox add on illustrates using the browser as a web composer rather
than only a navigator. To end, we evaluate MashQL on querying two data sets, DBLP and
DBPedia, and show that our indexing techniques allow instant user interaction.
118.A Dual Framework and Algorithms for Targeted Data
Delivery.
Synopsis:
A variety of emerging online data delivery applications challenge existing techniques for
data delivery to human users, applications, or middleware that are accessing data from
multiple autonomous servers. In this paper, we develop a framework for formalizing and
comparing pull-based solutions and present dual optimization approaches. The first
approach, most commonly used nowadays, maximizes user utility under the strict setting of
meeting a priori constraints on the usage of system resources. We present an alternative
and more flexible approach that maximizes user utility by satisfying all users. It does this
while minimizing the usage of system resources. We discuss the benefits of this latter
approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs).

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Through formal analysis, we identify sufficient optimality conditions for SUP. Using real
(RSS feeds) and synthetic traces, we empirically analyze the behavior of SUP under
varying conditions. Our experiments show that we can achieve a high degree of satisfaction
of user utility when the estimations of SUP closely estimate the real event stream, and has
the potential to save a significant amount of system resources. We further show that SUP
can exploit feedback to improve user utility with only a moderate increase in resource
utilization.
119.An Efﬁcient Certiﬁcateless Encryption for Secure Data
Sharing in Public Clouds..
Synopsis:
We propose a mediated certificateless encryption scheme without pairing operations for
securely sharing sensitive information in public clouds. Mediated certificateless public key
encryption (mCL-PKE) solves the key escrow problem in identity based encryption and
certificate revocation problem in public key cryptography. However, existing mCL-PKE
schemes are either inefficient because of the use of expensive pairing operations or
vulnerable against partial decryption attacks. In order to address the performance and
security issues, in this paper, we first propose a mCL-PKE scheme without using pairing
operations. We apply our mCL-PKE scheme to construct a practical solution to the problem
of sharing sensitive information in public clouds. The cloud is employed as a secure storage
as well as a key generation center. In our system, the data owner encrypts the sensitive
data using the cloud generated users' public keys based on its access control policies and
uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially
decrypts the encrypted data for the users. The users subsequently fully decrypt the partially
decrypted data using their private keys. The confidentiality of the content and the keys is
preserved with respect to the cloud, because the cloud cannot fully decrypt the information.
We also propose an extension to the above approach to improve the efficiency of
encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud
based system, and evaluate its security and performance. Our results show that our
schemes are efficient and practical.
120.Achieving Data Privacy through Secrecy Views and
Null-Based Virtual Updates.
Synopsis:
We may want to keep sensitive information in a relational database hidden from a user or
group thereof. We characterize sensitive data as the extensions of secrecy views. The
database, before returning the answers to a query posed by a restricted user, is updated to

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make the secrecy views empty or a single tuple with null values. Then, a query about any of
those views returns no meaningful information. Since the database is not supposed to be
physically changed for this purpose, the updates are only virtual, and also minimal.
Minimality makes sure that query answers, while being privacy preserving, are also
maximally informative. The virtual updates are based on null values as used in the SQL
standard. We provide the semantics of secrecy views, virtual updates, and secret answers
(SAs) to queries. The different instances resulting from the virtually updates are specified as
the models of a logic program with stable model semantics, which becomes the basis for
computation of the SAs.
121.A Survey of Indexing Techniques for Scalable Record
Linkage and Deduplication.
Synopsis:
Record linkage is the process of matching records from several databases that refer to the
same entities. When applied on a single database, this process is known as deduplication.
Increasingly, matched data are becoming important in many application areas, because
they can contain information that is not available otherwise, or that is too costly to acquire.
Removing duplicate records in a single database is a crucial step in the data cleaning
process, because duplicates can severely influence the outcomes of any subsequent data
processing or data mining. With the increasing size of today's databases, the complexity of
the matching process becomes one of the major challenges for record linkage and
deduplication. In recent years, various indexing techniques have been developed for record
linkage and deduplication. They are aimed at reducing the number of record pairs to be
compared in the matching process by removing obvious nonmatching pairs, while at the
same time maintaining high matching quality. This paper presents a survey of 12 variations
of 6 indexing techniques. Their complexity is analyzed, and their performance and
scalability is evaluated within an experimental framework using both synthetic and real data
sets. No such detailed survey has so far been published.
122.A Link Analysis Extension of Correspondence Analysis
for Mining Relational Databases.
Synopsis:
This work introduces a link analysis procedure for discovering relationships in a relational
database or a graph, generalizing both simple and multiple correspondence analysis. It is
based on a random walk model through the database defining a Markov chain having as
many states as elements in the database. Suppose we are interested in analyzing the
relationships between some elements (or records) contained in two different tables of the

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relational database. To this end, in a first step, a reduced, much smaller, Markov chain
containing only the elements of interest and preserving the main characteristics of the initial
chain, is extracted by stochastic complementation. This reduced chain is then analyzed by
projecting jointly the elements of interest in the diffusion map subspace and visualizing the
results. This two-step procedure reduces to simple correspondence analysis when only two
tables are defined, and to multiple correspondence analysis when the database takes the
form of a simple star-schema. On the other hand, a kernel version of the diffusion map
distance, generalizing the basic diffusion map distance to directed graphs, is also
introduced and the links with spectral clustering are discussed. Several data sets are
analyzed by using the proposed methodology, showing the usefulness of the technique for
extracting relationships in relational databases or graphs.
123.An Empirical Performance Evaluation of Relational
Keyword Search Systems.
Synopsis:
Extending the keyword search paradigm to relational data has been an active area of
research within the database and IR community during the past decade. Many approaches
have been proposed, but despite numerous publications, there remains a severe lack of
standardization for the evaluation of proposed search techniques. Lack of standardization
has resulted in contradictory results from different evaluations, and the numerous
discrepancies muddle what advantages are proffered by different approaches. In this paper,
we present the most extensive empirical performance evaluation of relational keyword
search techniques to appear to date in the literature. Our results indicate that many existing
search techniques do not provide acceptable performance for realistic retrieval tasks. In
particular, memory consumption precludes many search techniques from scaling beyond
small data sets with tens of thousands of vertices. We also explore the relationship between
execution time and factors varied in previous evaluations; our analysis indicates that most
of these factors have relatively little impact on performance. In summary, our work confirms
previous claims regarding the unacceptable performance of these search techniques and
underscores the need for standardization in evaluations--standardization exemplified by the
IR community.
124.Anonymization of Centralized and Distributed Social
Networks by Sequential Clustering.
Synopsis:
We study the problem of privacy-preservation in social networks. We consider the
distributed setting in which the network data is split between several data holders. The goal

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is to arrive at an anonymized view of the unified network without revealing to any of the data
holders information about links between nodes that are controlled by other data holders. To
that end, we start with the centralized setting and offer two variants of an anonymization
algorithm which is based on sequential clustering (Sq). Our algorithms significantly
outperform the SaNGreeA algorithm due to Campan and Truta which is the leading
algorithm for achieving anonymity in networks by means of clustering. We then devise
secure distributed versions of our algorithms. To the best of our knowledge, this is the first
study of privacy preservation in distributed social networks. We conclude by outlining future
research proposals in that direction.
125.Answering General Time-Sensitive Queries..
Synopsis:
Time is an important dimension of relevance for a large number of searches, such as over
blogs and news archives. So far, research on searching over such collections has largely
focused on locating topically similar documents for a query. Unfortunately, topic similarity
alone is not always sufficient for document ranking. In this paper, we observe that, for an
important class of queries that we call time-sensitive queries, the publication time of the
documents in a news archive is important and should be considered in conjunction with the
topic similarity to derive the final document ranking. Earlier work has focused on improving
retrieval for “recency” queries that target recent documents. We propose a more general
framework for handling time-sensitive queries and we automatically identify the important
time intervals that are likely to be of interest for a query. Then, we build scoring techniques
that seamlessly integrate the temporal aspect into the overall ranking mechanism. We
present an extensive experimental evaluation using a variety of news article data sets,
including TREC data as well as real web data analyzed using the Amazon Mechanical Turk.
We examine several techniques for detecting the important time intervals for a query over a
news archive and for incorporating this information in the retrieval process. We show that
our techniques are robust and significantly improve result quality for time-sensitive queries
compared to state-of-the-art retrieval techniques.
126.A Machine Learning Approach for Identifying Disease-
Treatment Relations in Short Texts.
Synopsis:
The Machine Learning (ML) field has gained its momentum in almost any domain of
research and just recently has become a reliable tool in the medical domain. The empirical
domain of automatic learning is used in tasks such as medical decision support, medical
imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient

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management care. ML is envisioned as a tool by which computer-based systems can be
integrated in the healthcare field in order to get a better, more efficient medical care. This
paper describes a ML-based methodology for building an application that is capable of
identifying and disseminating healthcare information. It extracts sentences from published
medical papers that mention diseases and treatments, and identifies semantic relations that
exist between diseases and treatments. Our evaluation results for these tasks show that the
proposed methodology obtains reliable outcomes that could be integrated in an application
to be used in the medical care domain. The potential value of this paper stands in the ML
settings that we propose and in the fact that we outperform previous results on the same
data set.
127.BestPeer++: A Peer-to-Peer Based Large-Scale Data
Processing Platform.
Synopsis:
The corporate network is often used for sharing information among the participating
companies and facilitating collaboration in a certain industry sector where companies share
a common interest. It can effectively help the companies to reduce their operational costs
and increase the revenues. However, the inter-company data sharing and processing poses
unique challenges to such a data management system including scalability, performance,
throughput, and security. In this paper, we present BestPeer++, a system which delivers
elastic data sharing services for corporate network applications in the cloud based on
BestPeer - a peer-to-peer (P2P) based data management platform. By integrating cloud
computing, database, and P2P technologies into one system, BestPeer++ provides an
economical, flexible and scalable platform for corporate network applications and delivers
data sharing services to participants based on the widely accepted pay-as-you-go business
model. We evaluate BestPeer++ on Amazon EC2 Cloud platform. The benchmarking
results show that BestPeer++ outperforms HadoopDB, a recently proposed large-scale data
processing system, in performance when both systems are employed to handle typical
corporate network workloads. The benchmarking results also demonstrate that BestPeer++
achieves near linear scalability for throughput with respect to the number of peer nodes.
128.Automatic Extraction of Top-k Lists from the Web.
Synopsis:
This paper is concerned with information extraction from top-k web pages, which are web
pages that describe top k instances of a topic which is of general interest. Examples include
“the 10 tallest buildings in the world”, “the 50 hits of 2010 you don't want to miss”, etc.
Compared to other structured information on the web (including web tables), information in

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top-k lists is larger and richer, of higher quality, and generally more interesting. Therefore
top-k lists are highly valuable. For example, it can help enrich open-domain knowledge
bases (to support applications such as search or fact answering). In this paper, we present
an efficient method that extracts top-k lists from web pages with high performance.
Specifically, we extract more than 1.7 million top-k lists from a web corpus of 1.6 billion
pages with 92.0% precision and 72.3% recall.
129.Bridging Social and Data Networks.
Synopsis:
Social networking applications have emerged as the platform of choice for carrying out a
number of different activities online. In addition to their primary target of social interaction,
we now also employ such applications to search for information online or to share
multimedia content with our friends and families. For instance, according to recent statistics,
each of us spends on average 15 min on YouTube every day.
130.A Privacy-Preserving Remote Data Integrity Checking
Protocol with Data Dynamics and Public Veriﬁability.
Synopsis:
Remote data integrity checking is a crucial technology in cloud computing. Recently, many
works focus on providing data dynamics and/or public verifiability to this type of protocols.
Existing protocols can support both features with the help of a third-party auditor. In a
previous work, Seb et al. propose a remote data integrity checking protocol that supports
data dynamics. In this paper, we adapt Seb et al.'s protocol to support public verifiability.
The proposed protocol supports public verifiability without help of a third-party auditor. In
addition, the proposed protocol does not leak any private information to third-party verifiers.
Through a formal analysis, we show the correctness and security of the protocol. After that,
through theoretical analysis and experimental results, we demonstrate that the proposed
protocol has a good performance.
131.Demand Bidding Program and Its Application in Hotel
Energy Management..
Synopsis:
Demand bidding program (DBP) is recently adopted in practice by some energy operators.
DBP is a risk-free demand response program targeting large energy consumers. In this

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paper, we consider DBP with the application in hotel energy management. For DBP,
optimization problem is formulated with the objective of maximizing expected reward, which
is received when the the amount of energy saving satisfies the contract. For a general
distribution of energy consumption, we give a general condition for the optimal bid and
outline an algorithm to find the solution without numerical integration. Furthermore, for
Gaussian distribution, we derive closed-form expressions of the optimal bid and the
corresponding expected reward. Regarding hotel energy, we characterize loads in the hotel
and introduce several energy consumption models that capture major energy use. With the
proposed models and DBP, simulation results show that DBP provides economics benefits
to the hotel and encourages load scheduling. Furthermore, when only mean and variance of
energy consumption are known, the validity of Gaussian approximation for computing
optimal load and expected reward is also discussed.
132.Constructing a Global Social Service Network for Better
Quality of Web Service Discovery.
Synopsis:
Web services have had a tremendous impact on the Web for supporting a distributed
service-based economy on a global scale. However, despite the outstanding progress, their
uptake on a Web scale has been significantly less than initially anticipated. The isolation of
services and the lack of social relationships among related services have been identified as
reasons for the poor uptake. In this paper, we propose connecting the isolated service
islands into a global social service network to enhance the services' sociability on a global
scale. First, we propose linked social service-specific principles based on linked data
principles for publishing services on the open Web as linked social services. Then, we
suggest a new framework for constructing the global social service network following linked
social service-specific principles based on complex network theories. Next, an approach is
proposed to enable the exploitation of the global social service network, providing Linked
Social Services as a Service. Finally, experimental results show that our approach can
solve the quality of service discovery problem, improving both the service discovering time
and the success rate by exploring service-to-service based on the global social service
network.
133.Computing Structural Statistics by Keywords in
Databases.
Synopsis:
Keyword search in RDBs has been extensively studied in recent years. The existing studies
focused on finding all or top-k interconnected tuple-structures that contain keywords. In

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reality, the number of such interconnected tuple-structures for a keyword query can be
large. It becomes very difficult for users to obtain any valuable information more than
individual interconnected tuple-structures. Also, it becomes challenging to provide a similar
mechanism like group-&-aggregate for those interconnected tuple-structures. In this paper,
we study computing structural statistics keyword queries by extending the group-&-
aggregate framework. We consider an RDB as a large directed graph where nodes
represent tuples, and edges represent the links among tuples. Instead of using tuples as a
member in a group to be grouped, we consider rooted subgraphs. Such a rooted subgraph
represents an interconnected tuple-structure among tuples and some of the tuples contain
keywords. The dimensions of the rooted subgraphs are determined by dimensional-
keywords in a data driven fashion. Two rooted subgraphs are grouped into the same group
if they are isomorphic based on the dimensions or in other words the dimensional-
keywords. The scores of the rooted subgraphs are computed by a user-given score function
if the rooted subgraphs contain some of general keywords. Here, the general keywords are
used to compute scores rather than determining dimensions. The aggregates are computed
using an SQL aggregate function for every group based on the scores computed. We give
our motivation using a real dataset. We propose new approaches to compute structural
statistics keyword queries, perform extensive performance studies using two large real
datasets and a large synthetic dataset, and confirm the effectiveness and efficiency of our
approach.
134. A Query Formulation Language for the Data Web.
Synopsis:
We present a query formulation language (called MashQL) in order to easily query and fuse
structured data on the web. The main novelty of MashQL is that it allows people with limited
IT skills to explore and query one (or multiple) data sources without prior knowledge about
the schema, structure, vocabulary, or any technical details of these sources. More
importantly, to be robust and cover most cases in practice, we do not assume that a data
source should have - an offline or inline - schema. This poses several language-design and
performance complexities that we fundamentally tackle. To illustrate the query formulation
power of MashQL, and without loss of generality, we chose the Data web scenario. We also
chose querying RDF, as it is the most primitive data model; hence, MashQL can be similarly
used for querying relational databases and XML. We present two implementations of
MashQL, an online mashup editor, and a Firefox add on. The former illustrates how
MashQL can be used to query and mash up the Data web as simple as filtering and piping
web feeds; and the Firefox add on illustrates using the browser as a web composer rather
than only a navigator. To end, we evaluate MashQL on querying two data sets, DBLP and
DBPedia, and show that our indexing techniques allow instant user interaction.
135.Dynamic Query Forms for Database Queries.

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Synopsis:
136.Constructing E-Tourism Platform Based on Service
Value Broker: A Knowledge Management Perspective..
Synopsis:
In our previous work, we have introduced various service value broker (SVB) patterns which
integrate business modeling, knowledge management and economic analysis. In this paper,
working towards the target of maximizing the potential usage of available resource to
achieve the optimization of the satisfaction on both the service provider side and the service
consumer side under the guidance of the public administrative, we propose to build the E-
Tourism platform based on SVB. This paper demonstrates the mechanism for SVB based
E-Tourism framework. The advantages of employing SVB include that the SVB can help to
increase the value added in a realtime and balanced manner which conforms to the
economical goal of both long run and short run. An experiment is shown using a personnel
recommendation system.
137.Decentralized Probabilistic Text Clustering.
Synopsis:
Text clustering is an established technique for improving quality in information retrieval, for
both centralized and distributed environments. However, traditional text clustering
algorithms fail to scale on highly distributed environments, such as peer-to-peer networks.

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Our algorithm for peer-to-peer clustering achieves high scalability by using a probabilistic
approach for assigning documents to clusters. It enables a peer to compare each of its
documents only with very few selected clusters, without significant loss of clustering quality.
The algorithm offers probabilistic guarantees for the correctness of each document
assignment to a cluster. Extensive experimental evaluation with up to 1 million peers and 1
million documents demonstrates the scalability and effectiveness of the algorithm.
138.Adaptive Fault Tolerant QoS Control Algorithms for
Maximizing.
Synopsis:
Data sensing and retrieval in wireless sensor systems have a widespread application in
areas such as security and surveillance monitoring, and command and control in
battlefields. In query-based wireless sensor systems, a user would issue a query and
expect a response to be returned within the deadline. While the use of fault tolerance
mechanisms through redundancy improves query reliability in the presence of unreliable
wireless communication and sensor faults, it could cause the energy of the system to be
quickly depleted. Therefore, there is an inherent trade-off between query reliability versus
energy consumption in query-based wireless sensor systems. In this paper, we develop
adaptive fault-tolerant quality of service (QoS) control algorithms based on hop-by-hop data
delivery utilizing “source” and “path” redundancy, with the goal to satisfy application QoS
requirements while prolonging the lifetime of the sensor system. We develop a
mathematical model for the lifetime of the sensor system as a function of system
parameters including the “source” and “path” redundancy levels utilized. We discover that
there exists optimal “source” and “path” redundancy under which the lifetime of the system
is maximized while satisfying application QoS requirements. Numerical data are presented
and validated through extensive simulation, with physical interpretations given, to
demonstrate the feasibility of our algorithm design.
139.Incremental Detection of Inconsistencies in Distributed
Data.
Synopsis:
This paper investigates incremental detection of errors in distributed data. Given a
distributed database D, a set Σ of conditional functional dependencies (CFDs), the set V of
violations of the CFDs in D, and updates ΔD to D, it is to find, with minimum data shipment,
changes ΔV to V in response to ΔD. The need for the study is evident since real-life data is
often dirty, distributed and frequently updated. It is often prohibitively expensive to
recompute the entire set of violations when D is updated. We show that the incremental

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detection problem is NP-complete for database D that is partitioned either vertically or
horizontally, even when Σ and D are fixed. Nevertheless, we show that it is bounded: there
exist algorithms to detect errors such that their computational cost and data shipment are
both linear in the size of ΔD and ΔV, independent of the size of the database D. We provide
such incremental algorithms for vertically partitioned data and horizontally partitioned data,
and show that the algorithms are optimal. We further propose optimization techniques for
the incremental algorithm over vertical partitions to reduce data shipment. We verify
experimentally, using real-life data on Amazon Elastic Compute Cloud (EC2), that our
algorithms substantially outperform their batch counterparts.
140.Cost-Based Optimization of Service Compositions.
Synopsis:
For providers of composite services, preventing cases of SLA violations is crucial. Previous
work has established runtime adaptation of compositions as a promising tool to achieve
SLA conformance. However, to get a realistic and complete view of the decision process of
service providers, the costs of adaptation need to be taken into account. In this paper, we
formalize the problem of finding the optimal set of adaptations, which minimizes the total
costs arising from SLA violations and the adaptations to prevent them. We present possible
algorithms to solve this complex optimization problem, and detail an end-to-end system
based on our earlier work on the PREvent (prediction and prevention based on event
monitoring) framework, which clearly indicates the usefulness of our model. We discuss
experimental results that show how the application of our approach leads to reduced costs
for the service provider, and explain the circumstances in which different algorithms lead to
more or less satisfactory results.
141.Combined Mining: Discovering Informative Knowledge
in Complex Data.
Synopsis:
Enterprise data mining applications often involve complex data such as multiple large
heterogeneous data sources, user preferences, and business impact. In such situations, a
single method or one-step mining is often limited in discovering informative knowledge. It
would also be very time and space consuming, if not impossible, to join relevant large data
sources for mining patterns consisting of multiple aspects of information. It is crucial to
develop effective approaches for mining patterns combining necessary information from
multiple relevant business lines, catering for real business settings and decision-making
actions rather than just providing a single line of patterns. The recent years have seen
increasing efforts on mining more informative patterns, e.g., integrating frequent pattern

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mining with classifications to generate frequent pattern-based classifiers. Rather than
presenting a specific algorithm, this paper builds on our existing works and proposes
combined mining as a general approach to mining for informative patterns combining
components from either multiple data sets or multiple features or by multiple methods on
demand. We summarize general frameworks, paradigms, and basic processes for
multifeature combined mining, multisource combined mining, and multimethod combined
mining. Novel types of combined patterns, such as incremental cluster patterns, can result
from such frameworks, which cannot be directly produced by the existing methods. A set of
real-world case studies has been conducted to test the frameworks, with some of them
briefed in this paper. They identify combined patterns for informing government debt
prevention and improving government service objectives, which show the flexibility and
instantiation capability of combined mining in discovering informative knowledge in complex
data.
142.Adaptive Provisioning of Human Expertise in Service-
oriented Systems.
Synopsis:
Web-based collaborations have become essential in today's business environments. Due to
the availability of various SOA frameworks, Web services emerged as the de facto
technology to realize flexible compositions of services. While most existing work focuses on
the discovery and composition of software based services, we highlight concepts for a
people-centric Web. Knowledge-intensive environments clearly demand for provisioning of
human expertise along with sharing of computing resources or business data through
software-based services. To address these challenges, we introduce an adaptive approach
allowing humans to provide their expertise through services using SOA standards, such as
WSDL and SOAP. The seamless integration of humans in the SOA loop triggers numerous
social implications, such as evolving expertise and drifting interests of human service
providers. Here we propose a framework that is based on interaction monitoring techniques
enabling adaptations in SOA-based socio-technical systems.
143.Keyword Query Routing.
Synopsis:
Keyword search is an intuitive paradigm for searching linked data sources on the web. We
propose to route keywords only to relevant sources to reduce the high cost of processing
keyword search queries over all sources. We propose a novel method for computing top-k
routing plans based on their potentials to contain results for a given keyword query. We
employ a keyword-element relationship summary that compactly represents relationships

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between keywords and the data elements mentioning them. A multilevel scoring mechanism
is proposed for computing the relevance of routing plans based on scores at the level of
keywords, data elements, element sets, and subgraphs that connect these elements.
Experiments carried out using 150 publicly available sources on the web showed that valid
plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be
computed in 1 second on average on a single PC. Further, we show routing greatly helps to
improve the performance of keyword search, without compromising its result quality.
144.Dynamic Query Forms for Database Queries..
Synopsis:
145.Effective Pattern Discovery for Text Mining.
Synopsis:
Many data mining techniques have been proposed for mining useful patterns in text
documents. However, how to effectively use and update discovered patterns is still an open
research issue, especially in the domain of text mining. Since most existing text mining
methods adopted term-based approaches, they all suffer from the problems of polysemy
and synonymy. Over the years, people have often held the hypothesis that pattern (or
phrase)-based approaches should perform better than the term-based ones, but many
experiments do not support this hypothesis. This paper presents an innovative and effective
pattern discovery technique which includes the processes of pattern deploying and pattern
evolving, to improve the effectiveness of using and updating discovered patterns for finding

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relevant and interesting information. Substantial experiments on RCV1 data collection and
TREC topics demonstrate that the proposed solution achieves encouraging performance.
146.A Personalized Ontology Model for Web Information
Gathering by Domain Specific Search.
Synopsis:
As a model for knowledge description and formalization, ontologies are widely used to
represent user profiles in personalized web information gathering. However, when
representing user profiles, many models have utilized only knowledge from either a global
knowledge base or a user local information. In this paper, a personalized ontology model is
proposed for knowledge representation and reasoning over user profiles. This model learns
ontological user profiles from both a world knowledge base and user local instance
repositories. The ontology model is evaluated by comparing it against benchmark models in
web information gathering. The results show that this ontology model is successful.
Distributed Databases.
Synopsis:
148.ELCA Evaluation for Keyword Search on Probabilistic
XML Data.
Synopsis:
As probabilistic data management is becoming one of the main research focuses and
keyword search is turning into a more popular query means, it is natural to think how to
support keyword queries on probabilistic XML data. With regards to keyword query on

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deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics
allows more relevant fragments rooted at the ELCAs to appear as results and is more
popular compared with other keyword query result semantics (such as SLCAs).
In this paper, we investigate how to evaluate ELCA results for keyword queries on
probabilistic XML documents. After defining probabilistic ELCA semantics in terms of
possible world semantics, we propose an approach to compute ELCA probabilities without
generating possible worlds. Then we develop an efficient stack-based algorithm that can
find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on
a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA
algorithm and compare it with its SLCA counterpart in aspects of result effectiveness, time
and space efficiency, and scalability.
149.Secure Efficient and Accurate Discovery of Patterns in
Sequence Data Sets.
Synopsis:
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a
large class of applications, such as biological DNA and protein motif mining, require efficient
mining of “approximate” patterns that are contiguous. The few existing algorithms that can
be applied to find such contiguous approximate pattern mining have drawbacks like poor
scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other
applications. In this paper, we present a new algorithm called FLexible and Accurate Motif
DEtector (FLAME). FLAME is a flexible suffix-tree-based algorithm that can be used to find
frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as
it always finds the pattern if it exists. Using both real and synthetic data sets, we
demonstrate that FLAME is fast, scalable, and outperforms existing algorithms on a variety
of performance metrics. In addition, based on FLAME, we also address a more general
problem, named extended structured motif extraction, which allows mining frequent
combinations of motifs under relaxed constraints.
150.Automatic Discovery of Personal Name Aliases from the
Web.
Synopsis:
An individual is typically referred by numerous name aliases on the web. Accurate
identification of aliases of a given person name is useful in various web related tasks such
as information retrieval, sentiment analysis, personal name disambiguation, and relation
extraction. We propose a method to extract aliases of a given personal name from the web.

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Given a personal name, the proposed method first extracts a set of candidate aliases.
Second, we rank the extracted candidates according to the likelihood of a candidate being a
correct alias of the given name. We propose a novel, automatically extracted lexical pattern-
based approach to efficiently extract a large set of candidate aliases from snippets retrieved
from a web search engine. We define numerous ranking scores to evaluate candidate
aliases using three approaches: lexical pattern frequency, word co-occurrences in an
anchor text graph, and page counts on the web. To construct a robust alias detection
system, we integrate the different ranking scores into a single ranking function using ranking
support vector machines. We evaluate the proposed method on three data sets: an English
personal names data set, an English place names data set, and a Japanese personal
names data set. The proposed method outperforms numerous baselines and previously
proposed name alias extraction methods, achieving a statistically significant mean
reciprocal rank (MRR) of 0.67. Experiments carried out using location names and Japanese
personal names suggest the possibility of extending the proposed method to extract aliases
for different types of named entities, and for different languages. Moreover, the aliases
extracted using the proposed method are successfully utilized in an information retrieval
task and improve recall by 20 percent in a relation-detection task.
151.Secure KNN Query Processing in Untrusted Cloud
Environments..
Synopsis:
Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access
information that is relevant to their present location. Users are interested in querying about
points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing
events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in
arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to
subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping
such information up-to-date and relevant to the users is not an easy task, so the owners of
such data sets will make the data accessible only to paying customers. Users send their
current location as the query parameter, and wish to receive as result the nearest POIs, i.e.,
nearest-neighbors (NNs). But typical data owners do not have the technical means to
support processing queries on a large scale, so they outsource data storage and querying
to a cloud service provider. Many such cloud providers exist who offer powerful storage and
computational infrastructures at low cost. However, cloud providers are not fully trusted, and
typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to
answer queries correctly, but they also collect the locations of the POIs and the subscribers
for other purposes. Leakage of POI locations can lead to privacy breaches as well as
financial losses to the data owners, for whom the POI data set is an important source of
revenue. Disclosure of user locations leads to privacy violations and may deter subscribers
from using the service altogether. In this paper, we propose a family of techniques that

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allow processing of NN queries in an untrusted outsourced environment, while at the same
time protecting both the POI and querying users' positions. Our techniques rely on mutable
order preserving encoding (mOPE), the only secure order-preserving encryption method
known to-date. W- also provide performance optimizations to decrease the computational
cost inherent to processing on encrypted data, and we consider the case of incrementally
updating data sets. We present an extensive performance evaluation of our techniques to
illustrate their viability in practice.
152.Evaluating the Vulnerability of Network Mechanisms to
Sophisticated DDoS Attacks.
Synopsis:
In recent years, we have experienced a wave of DDoS attacks threatening the welfare of
the internet. These are launched by malicious users whose only incentive is to degrade the
performance of other, innocent, users. The traditional systems turn out to be quite
vulnerable to these attacks. The objective of this work is to take a first step to close this
fundamental gap, aiming at laying a foundation that can be used in future computer/network
designs taking into account the malicious users. Our approach is based on proposing a
metric that evaluates the vulnerability of a system. We then use our vulnerability metric to
evaluate a data structure which is commonly used in network mechanisms-the Hash table
data structure. We show that Closed Hash is much more vulnerable to DDoS attacks than
Open Hash, even though the two systems are considered to be equivalent by traditional
performance evaluation. We also apply the metric to queuing mechanisms common to
computer and communications systems. Furthermore, we apply it to the practical case of a
hash table whose requests are controlled by a queue, showing that even after the attack
has ended, the regular users still suffer from performance degradation or even a total denial
of service.
153.Efﬁcient audit service outsourcing for data integrity in
clouds.
Synopsis:
Cloud-based outsourced storage relieves the client's burden for storage management and
maintenance by providing a comparably low-cost, scalable, location-independent platform.
However, the fact that clients no longer have physical possession of data indicates that they
are facing a potentially formidable risk for missing or corrupted data. To avoid the security
risks, audit services are critical to ensure the integrity and availability of outsourced data
and to achieve digital forensics and credibility on cloud computing. Provable data

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possession (PDP), which is a cryptographic technique for verifying the integrity of data
without retrieving it at an untrusted server, can be used to realize audit services.
In this paper, profiting from the interactive zero-knowledge proof system, we address the
construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness
property) and the leakage of verified data (zero-knowledge property). We prove that our
construction holds these properties based on the computation Diffie–Hellman assumption
and the rewindable black-box knowledge extractor. We also propose an efficient
mechanism with respect to probabilistic queries and periodic verification to reduce the audit
costs per verification and implement abnormal detection timely. In addition, we present an
efficient method for selecting an optimal parameter value to minimize computational
overheads of cloud audit services. Our experimental results demonstrate the effectiveness
of our approach.
154.Bridging Socially-Enhanced Virtual Communities.
Synopsis:
Interactions spanning multiple organizations have become an important aspect in today’s
collaboration landscape. Organizations create alliances to fulfill strategic objectives. The
dynamic nature of collaborations increasingly demands for automated techniques and
algorithms to support the creation of such alliances. Our approach bases on the
recommendation of potential alliances by discovery of currently relevant competence
sources and the support of semi-automatic formation. The environment is service-oriented
comprising humans and software services with distinct capabilities. To mediate between
previously separated groups and organizations, we introduce the broker concept that
bridges disconnected networks. We present a dynamic broker discovery approach based on
interaction mining techniques and trust metrics.
155.The Role of Hubness in Clustering High-Dimensional
Data.
Synopsis:
High-dimensional data arise naturally in many domains, and have regularly presented a
great challenge for traditional data mining techniques, both in terms of effectiveness and
efficiency. Clustering becomes difficult due to the increasing sparsity of such data, as well
as the increasing difficulty in distinguishing distances between data points. In this paper, we
take a novel perspective on the problem of clustering high-dimensional data. Instead of
attempting to avoid the curse of dimensionality by observing a lower dimensional feature

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subspace, we embrace dimensionality by taking advantage of inherently high-dimensional
phenomena. More specifically, we show that hubness, i.e., the tendency of high-
dimensional data to contain points (hubs) that frequently occur in k-nearest-neighbor lists of
other points, can be successfully exploited in clustering. We validate our hypothesis by
demonstrating that hubness is a good measure of point centrality within a high-dimensional
data cluster, and by proposing several hubness-based clustering algorithms, showing that
major hubs can be used effectively as cluster prototypes or as guides during the search for
centroid-based cluster configurations. Experimental results demonstrate good performance
of our algorithms in multiple settings, particularly in the presence of large quantities of noise.
The proposed methods are tailored mostly for detecting approximately hyperspherical
clusters and need to be extended to properly handle clusters of arbitrary shapes.
156.Exploring the Impact of Information System
Introduction: The Case of an Australian Hospital
Emergency Department .
Synopsis:
In recent years, a large number of healthcare organisations have adopted information
systems to improve their operations. An analysis of the existing literature shows that there is
little concrete understanding about information systemsâ impact within the
healthcare context. This study aims to improve the current understanding in the field by
conducting an in-depth, exploratory study of the impact of IS in the healthcare industry. A
longitudinal single case study was carried out in a major emergency and trauma centre in
Australia, which just recently introduced a large scale IS. By focusing on a number of key
work routines involved in the case organisation, this study gathered rich and deep insights
into how the introduction of an advanced IS affects what healthcare professionals do as well
as how they work and think. The findings of this study offer some important implications to
both research and practice.
157.Efficient Data Mining for XML Queries – Answering
Support..
Synopsis:
Extracting information from semistructured documents is a very hard task, and is going to
become more and more critical as the amount of digital information available on the Internet
grows. Indeed, documents are often so large that the data set returned as answer to a
query may be too big to convey interpretable knowledge. In this paper, we describe an
approach based on Tree-Based Association Rules (TARs): mined rules, which provide

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approximate, intensional information on both the structure and the contents of Extensible
Markup Language (XML) documents, and can be stored in XML format as well. This mined
knowledge is later used to provide: 1) a concise idea—the gist—of both the structure and
the content of the XML document and 2) quick, approximate answers to queries. In this
paper, we focus on the second feature. A prototype system and experimental results
demonstrate the effectiveness of the approach.
158.Collaborative Filtering with Personalized Skylines.
Synopsis:
Collaborative filtering (CF) systems exploit previous ratings and similarity in user behavior to
recommend the top-k objects/records which are potentially most interesting to the user
assuming a single score per object. However, in various applications, a record (e.g., hotel)
maybe rated on several attributes (value, service, etc.), in which case simply returning the
ones with the highest overall scores fails to capture the individual attribute characteristics
and to accommodate different selection criteria. In order to enhance the flexibility of CF, we
propose Collaborative Filtering Skyline (CFS), a general framework that combines the
advantages of CF with those of the skyline operator. CFS generates a personalized skyline
for each user based on scores of other users with similar behavior. The personalized
skyline includes objects that are good on certain aspects, and eliminates the ones that are
not interesting on any attribute combination. Although the integration of skylines and CF has
several attractive properties, it also involves rather expensive computations. We face this
challenge through a comprehensive set of algorithms and optimizations that reduce the cost
of generating personalized skylines. In addition to exact skyline processing, we develop an
approximate method that provides error guarantees. Finally, we propose the top-k
personalized skyline, where the user specifies the required output cardinality.
159.Web Image Re-Ranking UsingQuery-Specific Semantic
Signatures.
Synopsis:
Image re-ranking, as an effective way to improve the results of web-based image search,
has been adopted by current commercial search engines such as Bing and Google. Given a
query keyword, a pool of images are first retrieved based on textual information. By asking
the user to select a query image from the pool, the remaining images are re-ranked based
on their visual similarities with the query image. A major challenge is that the similarities of
visual features do not well correlate with images' semantic meanings which interpret users'
search intention. Recently people proposed to match images in a semantic space which
used attributes or reference classes closely related to the semantic meanings of images as

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basis. However, learning a universal visual semantic space to characterize highly diverse
images from the web is difficult and inefficient. In this paper, we propose a novel image re-
ranking framework, which automatically offline learns different semantic spaces for different
query keywords. The visual features of images are projected into their related semantic
spaces to get semantic signatures. At the online stage, images are re-ranked by comparing
their semantic signatures obtained from the semantic space specified by the query keyword.
The proposed query-specific semantic signatures significantly improve both the accuracy
and efficiency of image re-ranking. The original visual features of thousands of dimensions
can be projected to the semantic signatures as short as 25 dimensions. Experimental
results show that 25-40 percent relative improvement has been achieved on re-ranking
precisions compared with the state-of-the-art methods.
160.Ginix Generalized Inverted Index for Keyword Search.
Synopsis:
Keyword search has become a ubiquitous method for users to access text data in the face
of information explosion. Inverted lists are usually used to index underlying documents to
retrieve documents according to a set of keywords efficiently. Since inverted lists are
usually large, many compression techniques have been proposed to reduce the storage
space and disk I/O time. However, these techniques usually perform decompression
operations on the fly, which increases the CPU time. This paper presents a more efficient
index structure, the Generalized INverted IndeX (Ginix), which merges consecutive IDs in
inverted lists into intervals to save storage space. With this index structure, more efficient
algorithms can be devised to perform basic keyword search operations, i.e., the union and
the intersection operations, by taking the advantage of intervals. Specifically, these
algorithms do not require conversions from interval lists back to ID lists. As a result,
keyword search using Ginix can be more efficient than those using traditional inverted
indices. The performance of Ginix is also improved by reordering the documents in datasets
using two scalable algorithms. Experiments on the performance and scalability of Ginix on
real datasets show that Ginix not only requires less storage space, but also improves the
keyword search performance, compared with traditional inverted indexes.
161.Generative Models for Item Adoptions Using Social
Correlation.
Synopsis:
Users face many choices on the web when it comes to choosing which product to buy,
which video to watch, and so on. In making adoption decisions, users rely not only on their
own preferences, but also on friends. We call the latter social correlation, which may be

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caused by the homophily and social influence effects. In this paper, we focus on modeling
social correlation on users item adoptions. Given a user-user social graph and an item-user
adoption graph, our research seeks to answer the following questions: Whether the items
adopted by a user correlate with items adopted by her friends, and how to model item
adoptions using social correlation. We propose a social correlation framework that
considers a social correlation matrix representing the degrees of correlation from every user
to the users friends, in addition to a set of latent factors representing topics of interests of
individual users. Based on the framework, we develop two generative models, namely
sequential and unified, and the corresponding parameter estimation approaches. From
each model, we devise the social correlation only and hybrid methods for predicting missing
adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed
models outperform the approach based on latent factors only (LDA).
162.Cost-aware rank join with random and sorted access.
Synopsis:
In this paper, we address the problem of joining ranked results produced by two or more
services on the web. We consider services endowed with two kinds of access that are often
available: 1) sorted access, which returns tuples sorted by score; 2) random access, which
returns tuples matching a given join attribute value. Rank join operators combine objects of
two or more relations and output the k combinations with the highest aggregate score.
While the past literature has studied suitable bounding schemes for this setting, in this
paper we focus on the definition of a pulling strategy, which determines the order of
invocation of the joined services. We propose the Cost-Aware with Random and Sorted
access (CARS) pulling strategy, which is derived at compile-time and is oblivious of the
query-dependent score distributions. We cast CARS as the solution of an optimization
problem based on a small set of parameters characterizing the joined services. We validate
the proposed strategy with experiments on both real and synthetic data sets. We show that
CARS outperforms prior proposals and that its overall access cost is always within a very
short margin from that of an oracle-based optimal strategy. In addition, CARS is shown to
be robust w.r.t. the uncertainty that may characterize the estimated parameters.
163.One Size Does Not Fit All: Towards User- and Query-
Dependent Ranking For Web Databases.
Synopsis:
With the emergence of the deep web, searching web databases in domains such as
vehicles, real estate, etc., has become a routine task. One of the problems in this context is
ranking the results of a user query. Earlier approaches for addressing this problem have

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used frequencies of database values, query logs, and user profiles. A common thread in
most of these approaches is that ranking is done in a user- and/or query-independent
manner. This paper proposes a novel query- and user-dependent approach for ranking
query results in web databases. We present a ranking model, based on two complementary
notions of user and query similarity, to derive a ranking function for a given user query. This
function is acquired from a sparse workload comprising of several such ranking functions
derived for various user-query pairs. The model is based on the intuition that similar users
display comparable ranking preferences over the results of similar queries. We define these
similarities formally in alternative ways and discuss their effectiveness analytically and
experimentally over two distinct web databases.
164. Gmatch: Secure and Privacy-Preserving Group
Matching in Social Networks..
Synopsis:
Groups are becoming one of the most compelling features in both online social networks
and Twitter-like micro-blogging services. A stranger outside of an existing group may have
the need to find out more information about attributes of current members in the group, in
order to make a decision to join. However, in many cases, attributes of both group members
and the stranger need to be kept private and should not be revealed to others, as they may
contain sensitive and personal information. How can we find out matching information exists
between the stranger and members of the group, based on their attributes that are not to be
disclosed? In this paper, we present a new group matching mechanism, by taking
advantage private set intersection and ring signatures. With our scheme, a stranger is able
to collect correct group matching information while sensitive information of the stranger and
group members are not disclosed. Finally, we propose to use batch verification to
significantly improve the performance of the matching process.
165.Heuristics Based Query Processing for Large RDF
Graphs Using Cloud Computing.
Synopsis:
Semantic web is an emerging area to augment human reasoning. Various technologies are
being developed in this arena which have been standardized by the World Wide Web
Consortium (W3C). One such standard is the Resource Description Framework (RDF).
Semantic web technologies can be utilized to build efficient and scalable systems for Cloud
Computing. With the explosion of semantic web technologies, large RDF graphs are
common place. This poses significant challenges for the storage and retrieval of RDF
graphs. Current frameworks do not scale for large RDF graphs and as a result do not

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address these challenges. In this paper, we describe a framework that we built using
Hadoop to store and retrieve large numbers of RDF triples by exploiting the cloud
computing paradigm. We describe a scheme to store RDF data in Hadoop Distributed File
System. More than one Hadoop job (the smallest unit of execution in Hadoop) may be
needed to answer a query because a single triple pattern in a query cannot simultaneously
take part in more than one join in a single Hadoop job. To determine the jobs, we present
an algorithm to generate query plan, whose worst case cost is bounded, based on a greedy
approach to answer a SPARQL Protocol and RDF Query Language (SPARQL) query. We
use Hadoop's MapReduce framework to answer the queries. Our results show that we can
store large RDF graphs in Hadoop clusters built with cheap commodity class hardware.
Furthermore, we show that our framework is scalable and efficient and can handle large
amounts of RDF data, unlike traditional approaches.
166.Data Leakage Detection.
Synopsis:
We study the following problem: A data distributor has given sensitive data to a set of
supposedly trusted agents (third parties). Some of the data are leaked and found in an
unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess
the likelihood that the leaked data came from one or more agents, as opposed to having
been independently gathered by other means. We propose data allocation strategies
(across the agents) that improve the probability of identifying leakages. These methods do
not rely on alterations of the released data (e.g., watermarks). In some cases, we can also
inject “realistic but fake” data records to further improve our chances of detecting leakage
and identifying the guilty party.
167.Optimal Service Pricing for a Cloud Cache.
Synopsis:
Cloud applications that offer data management services are emerging. Such clouds support
caching of data in order to provide quality query services. The users can query the cloud
data, paying the price for the infrastructure they use. Cloud management necessitates an
economy that manages the service of multiple users in an efficient, but also, resource-
economic way that allows for cloud profit. Naturally, the maximization of cloud profit given
some guarantees for user satisfaction presumes an appropriate price-demand model that
enables optimal pricing of query services. The model should be plausible in that it reflects
the correlation of cache structures involved in the queries. Optimal pricing is achieved
based on a dynamic pricing scheme that adapts to time changes. This paper proposes a
novel price-demand model designed for a cloud cache and a dynamic pricing scheme for
queries executed in the cloud cache. The pricing solution employs a novel method that

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estimates the correlations of the cache services in an time-efficient manner. The
experimental study shows the efficiency of the solution.
168.Mining Order-Preserving Submatrices from Data with
Repeated Measurements.
Synopsis:
Order-preserving submatrices (OPSM's) have been shown useful in capturing concurrent
patterns in data when the relative magnitudes of data items are more important than their
exact values. For instance, in analyzing gene expression profiles obtained from microarray
experiments, the relative magnitudes are important both because they represent the change
of gene activities across the experiments, and because there is typically a high level of
noise in data that makes the exact values untrustable. To cope with data noise, repeated
experiments are often conducted to collect multiple measurements. We propose and study
a more robust version of OPSM, where each data item is represented by a set of values
obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with
repeated measurements). We define OPSM-RM based on a number of practical
requirements. We discuss the computational challenges of OPSM-RM and propose a
generic mining algorithm. We further propose a series of techniques to speed up two time
dominating components of the algorithm. We show the effectiveness and efficiency of our
methods through a series of experiments conducted on real microarray data.
169.Identifying Content for Planned Events Across Social
Media Sites.
Synopsis:
User-contributed Web data contains rich and diverse information about a variety of events
in the physical world, such as shows, festivals, conferences and more. This information
ranges from known event features (e.g., title, time, location) posted on event aggregation
platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions
related to events shared on different social media sites (e.g., Twitter, YouTube, Flickr). In
this paper, we focus on the challenge of automatically identifying user-contributed content
for events that are planned and, therefore, known in advance, across different social media
sites. We mine event aggregation platforms to extract event features, which are often noisy
or missing. We use these features to develop query formulation strategies for retrieving
content associated with an event on different social media sites. Further, we explore ways
in which event content identified on one social media site can be used to retrieve additional
relevant event content on other social media sites. We apply our strategies to a large set of

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user-contributed events, and analyze their effectiveness in retrieving relevant event content
from Twitter, YouTube, and Flickr.
170.Efficient and Accurate Discovery of Patterns in
Sequence Data Sets.
Synopsis:
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a
large class of applications, such as biological DNA and protein motif mining, require efficient
mining of “approximate” patterns that are contiguous. The few existing algorithms that can
be applied to find such contiguous approximate pattern mining have drawbacks like poor
scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other
applications. In this paper, we present a new algorithm called FLexible and Accurate Motif
DEtector (FLAME). FLAME is a flexible suffix-tree-based algorithm that can be used to find
frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as
it always finds the pattern if it exists. Using both real and synthetic data sets, we
demonstrate that FLAME is fast, scalable, and outperforms existing algorithms on a variety
of performance metrics. In addition, based on FLAME, we also address a more general
problem, named extended structured motif extraction, which allows mining frequent
combinations of motifs under relaxed constraints.
171.Optimal Stochastic Location Updates In Mobile Ad Hoc
Networks.
Synopsis:
We consider the location service in a mobile ad-hoc network (MANET), where each node
needs to maintain its location information by 1) frequently updating its location information
within its neighboring region, which is called neighborhood update (NU), and 2) occasionally
updating its location information to certain distributed location server in the network, which
is called location server update (LSU). The trade off between the operation costs in location
updates and the performance losses of the target application due to location inaccuracies
(i.e., application costs) imposes a crucial question for nodes to decide the optimal strategy
to update their location information, where the optimality is in the sense of minimizing the
overall costs. In this paper, we develop a stochastic sequential decision framework to
analyze this problem. Under a Markovian mobility model, the location update decision
problem is modeled as a Markov Decision Process (MDP). We first investigate the
monotonicity properties of optimal NU and LSU operations with respect to location
inaccuracies under a general cost setting. Then, given a separable cost structure, we show
that the location update decisions of NU and LSU can be independently carried out without

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loss of optimality, i.e., a separation property. From the discovered separation property of the
problem structure and the monotonicity properties of optimal actions, we find that 1) there
always exists a simple optimal threshold-based update rule for LSU operations; 2) for NU
operations, an optimal threshold-based update rule exists in a low-mobility scenario. In the
case that no a priori knowledge of the MDP model is available, we also introduce a practical
model-free learning approach to find a near-optimal solution for the problem.
172.SAMPLING ONLINE SOCIAL NETWORK.
Synopsis:
As online social networking emerges, there has been increased interest to utilize the
underlying network structure as well as the available information on social peers to improve
the information needs of a user. In this paper, we focus on improving the performance of
information collection from the neighborhood of a user in a dynamic social network. We
introduce sampling-based algorithms to efficiently explore a user's social network
respecting its structure and to quickly approximate quantities of interest. We introduce and
analyze variants of the basic sampling scheme exploring correlations across our samples.
Models of centralized and distributed social networks are considered. We show that our
algorithms can be utilized to rank items in the neighborhood of a user, assuming that
information for each user in the network is available. Using real and synthetic data sets, we
validate the results of our analysis and demonstrate the efficiency of our algorithms in
approximating quantities of interest. The methods we describe are general and can
probably be easily adopted in a variety of strategies aiming to efficiently collect information
from a social graph.
173.Improving Aggregate Recommendation Diversity Using
Ranking-Based Techniques.
Synopsis:
Recommender systems are becoming increasingly important to individual users and
businesses for providing personalized recommendations. However, while the majority of
algorithms proposed in recommender systems literature have focused on improving
recommendation accuracy (as exemplified by the recent Netflix Prize competition), other
important aspects of recommendation quality, such as the diversity of recommendations,
have often been overlooked. In this paper, we introduce and explore a number of item
ranking techniques that can generate substantially more diverse recommendations across
all users while maintaining comparable levels of recommendation accuracy. Comprehensive
empirical evaluation consistently shows the diversity gains of the proposed techniques
using several real-world rating data sets and different rating prediction algorithms..

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174.Exploring Application-Level Semantics for Data
Compression.
Synopsis:
Natural phenomena show that many creatures form large social groups and move in regular
patterns. However, previous works focus on finding the movement patterns of each single
object or all objects. In this paper, we first propose an efficient distributed mining algorithm
to jointly identify a group of moving objects and discover their movement patterns in
wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D,
which exploits the obtained group movement patterns to reduce the amount of delivered
data. The compression algorithm includes a sequence merge and an entropy reduction
phases. In the sequence merge phase, we propose a Merge algorithm to merge and
compress the location data of a group of moving objects. In the entropy reduction phase, we
formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that
obtains the optimal solution. Moreover, we devise three replacement rules and derive the
maximum compression ratio. The experimental results show that the proposed compression
algorithm leverages the group movement patterns to reduce the amount of delivered data
effectively and efficiently.
175.Publishing Search Logs A Comparative Study of
Privacy Guarantees.
Synopsis:
Search engine companies collect the “database of intentions,” the histories of their users'
search queries. These search logs are a gold mine for researchers. Search engine
companies, however, are wary of publishing search logs in order not to disclose sensitive
information. In this paper, we analyze algorithms for publishing frequent keywords, queries,
and clicks of a search log. We first show how methods that achieve variants of k-anonymity
are vulnerable to active attacks. We then demonstrate that the stronger guarantee ensured
by ε-differential privacy unfortunately does not provide any utility for this problem. We then
propose an algorithm ZEALOUS and show how to set its parameters to achieve (ε, δ)-
probabilistic privacy. We also contrast our analysis of ZEALOUS with an analysis by
Korolova et al. [17] that achieves (ε',δ')-indistinguishability. Our paper concludes with a
large experimental study using real applications where we compare ZEALOUS and
previous work that achieves k-anonymity in search log publishing. Our results show that
ZEALOUS yields comparable utility to k-anonymity while at the same time achieving much
stronger privacy guarantees.

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Distributed Databases..
Synopsis:
177.Joint Top-K Spatial Keyword Query Processing.
Synopsis:
Web users and content are increasingly being geopositioned, and increased focus is being
given to serving local content in response to web queries. This development calls for spatial
keyword queries that take into account both the locations and textual descriptions of
content. We study the efficient, joint processing of multiple top-k spatial keyword queries.
Such joint processing is attractive during high query loads and also occurs when multiple
queries are used to obfuscate a user's true query. We propose a novel algorithm and index
structure for the joint processing of top-k spatial keyword queries. Empirical studies show
that the proposed solution is efficient on real data sets. We also offer analytical studies on
synthetic data sets to demonstrate the efficiency of the proposed solution.
178.Heuristics Based Query Processing for Large RDF
Graphs Using Cloud Computing.
Synopsis:
Semantic web is an emerging area to augment human reasoning. Various technologies are
being developed in this arena which have been standardized by the World Wide Web
Consortium (W3C). One such standard is the Resource Description Framework (RDF).
Semantic web technologies can be utilized to build efficient and scalable systems for Cloud
Computing. With the explosion of semantic web technologies, large RDF graphs are
common place. This poses significant challenges for the storage and retrieval of RDF

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graphs. Current frameworks do not scale for large RDF graphs and as a result do not
address these challenges. In this paper, we describe a framework that we built using
Hadoop to store and retrieve large numbers of RDF triples by exploiting the cloud
computing paradigm. We describe a scheme to store RDF data in Hadoop Distributed File
System. More than one Hadoop job (the smallest unit of execution in Hadoop) may be
needed to answer a query because a single triple pattern in a query cannot simultaneously
take part in more than one join in a single Hadoop job. To determine the jobs, we present
an algorithm to generate query plan, whose worst case cost is bounded, based on a greedy
approach to answer a SPARQL Protocol and RDF Query Language (SPARQL) query. We
use Hadoop's MapReduce framework to answer the queries. Our results show that we can
store large RDF graphs in Hadoop clusters built with cheap commodity class hardware.
Furthermore, we show that our framework is scalable and efficient and can handle large
amounts of RDF data, unlike traditional approaches.
179.Query Planning for Continuous Aggregation Queries
over a Network of Data Aggregators.
Synopsis:
Continuous queries are used to monitor changes to time varying data and to provide results
useful for online decision making. Typically a user desires to obtain the value of some
aggregation function over distributed data items, for example, to know value of portfolio for
a client; or the AVG of temperatures sensed by a set of sensors. In these queries a client
specifies a coherency requirement as part of the query. We present a low-cost, scalable
technique to answer continuous aggregation queries using a network of aggregators of
dynamic data items. In such a network of data aggregators, each data aggregator serves a
set of data items at specific coherencies. Just as various fragments of a dynamic webpage
are served by one or more nodes of a content distribution network, our technique involves
decomposing a client query into subqueries and executing subqueries on judiciously chosen
data aggregators with their individual subquery incoherency bounds. We provide a
technique for getting the optimal set of subqueries with their incoherency bounds which
satisfies client query's coherency requirement with least number of refresh messages sent
from aggregators to the client. For estimating the number of refresh messages, we build a
query cost model which can be used to estimate the number of messages required to
satisfy the client specified incoherency bound. Performance results using real-world traces
show that our cost-based query planning leads to queries being executed using less than
one third the number of messages required by existing schemes.
180.SeDas: A Self-Destructing Data System Based on Active
Storage Framework.

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Synopsis:
Personal data stored in the Cloud may contain account numbers, passwords, notes, and
other important information that could be used and misused by a miscreant, a competitor, or
a court of law. These data are cached, copied, and archived by Cloud Service Providers
(CSPs), often without users' authorization and control. Self-destructing data mainly aims at
protecting the user data's privacy. All the data and their copies become destructed or
unreadable after a user-specified time, without any user intervention. In addition, the
decryption key is destructed after the user-specified time. In this paper, we present SeDas,
a system that meets this challenge through a novel integration of cryptographic techniques
with active storage techniques based on T10 OSD standard. We implemented a proof-of-
concept SeDas prototype. Through functionality and security properties evaluations of the
SeDas prototype, the results demonstrate that SeDas is practical to use and meets all the
privacy-preserving goals described. Compared to the system without self-destructing data
mechanism, throughput for uploading and downloading with the proposed SeDas
acceptably decreases by less than 72%, while latency for upload/download operations with
self-destructing data mechanism increases by less than 60%.
181.Mining Web Graphs for Recommendations.
Synopsis:
As the exponential explosion of various contents generated on the Web, Recommendation
techniques have become increasingly indispensable. Innumerable different kinds of
recommendations are made on the Web every day, including movies, music, images, books
recommendations, query suggestions, tags recommendations, etc. No matter what types of
data sources are used for the recommendations, essentially these data sources can be
modeled in the form of various types of graphs. In this paper, aiming at providing a general
framework on mining Web graphs for recommendations, (1) we first propose a novel
diffusion method which propagates similarities between different nodes and generates
recommendations; (2) then we illustrate how to generalize different recommendation
problems into our graph diffusion framework. The proposed framework can be utilized in
many recommendation tasks on the World Wide Web, including query suggestions, tag
recommendations, expert finding, image recommendations, image annotations, etc. The
experimental analysis on large data sets shows the promising future of our work.
182.Horizontal Aggregations in SQL to Prepare Data Sets
for Data Mining Analysis..
Synopsis:

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Preparing a data set for analysis is generally the most time consuming task in a data mining
project, requiring many complex SQL queries, joining tables, and aggregating columns.
Existing SQL aggregations have limitations to prepare data sets because they return one
column per aggregated group. In general, a significant manual effort is required to build
data sets, where a horizontal layout is required. We propose simple, yet powerful, methods
to generate SQL code to return aggregated columns in a horizontal tabular layout, returning
a set of numbers instead of one number per row. This new class of functions is called
horizontal aggregations. Horizontal aggregations build data sets with a horizontal
denormalized layout (e.g., point-dimension, observation-variable, instance-feature), which is
the standard layout required by most data mining algorithms. We propose three
fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the
programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ
queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments
with large tables compare the proposed query evaluation methods. Our CASE method has
similar speed to the PIVOT operator and it is much faster than the SPJ method. In general,
the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.
183.Scalable Learning of Collective Behavior.
Synopsis:
This study of collective behavior is to understand how individuals behave in a social
networking environment. Oceans of data generated by social media like Facebook, Twitter,
Flickr, and YouTube present opportunities and challenges to study collective behavior on a
large scale. In this work, we aim to learn to predict collective behavior in social media. In
particular, given information about some individuals, how can we infer the behavior of
unobserved individuals in the same network? A social-dimension-based approach has been
shown effective in addressing the heterogeneity of connections presented in social media.
However, the networks in social media are normally of colossal size, involving hundreds of
thousands of actors. The scale of these networks entails scalable learning of models for
collective behavior prediction. To address the scalability issue, we propose an edge-centric
clustering scheme to extract sparse social dimensions. With sparse social dimensions, the
proposed approach can efficiently handle networks of millions of actors while demonstrating
a comparable prediction performance to other nonscalable methods.
184.SUSIE: Search Using Services and Information
Extraction.
Synopsis:

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The API of a Web service restricts the types of queries that the service can answer. For
example, a Web service might provide a method that returns the songs of a given singer,
but it might not provide a method that returns the singers of a given song. If the user asks
for the singer of some specific song, then the Web service cannot be called - even though
the underlying database might have the desired piece of information. This asymmetry is
particularly problematic if the service is used in a Web service orchestration system. In this
paper, we propose to use on-the-fly information extraction to collect values that can be used
as parameter bindings for the Web service. We show how this idea can be integrated into a
Web service orchestration system. Our approach is fully implemented in a prototype called
SUSIE. We present experiments with real-life data and services to demonstrate the
practical viability and good performance of our approach.
185.Optimization of Horizontal Aggregation in SQL by
Using K-Means Clustering.
Synopsis:
Data mining systems use datasets with columns in a horizontal tabular layout in order to
analyze data efficiently. In data mining project, preparation of data set is more complex
process and it requires many SQL queries, joining tables and aggregating columns. So this
is an important problem in data mining. Horizontal aggregation solves this problem by
preparing the data set in a horizontal tabular layout and returns a set of numbers instead of
single number per row. Integrating data mining algorithms with a relational database
management system is an important problem for database programmers. K means
algorithms using SQL is a best clustering algorithm[10]. When K means algorithm use with
horizontal aggregation, it partitions the large set of data get from the horizontal aggregation
into k cluster in order to reduce effort in data preparation phase of data mining. Here
describing three SQL implementations of K means algorithm to integrate it with a relational
database management systems: 1) Standard K Means, a direct translation of K means into
SQL, 2) Optimized K means, an optimized version based on improved data organization,
efficient indexing and sufficient statistics and 3) Incremental K means which is an
incremental version that uses the optimized version as a building block with fast
convergence and automated reseeding. Horizontal aggregation solves problem of data
mining in preparing summary data set and K means clustering algorithm integrated with a
relational DBMS using SQL optimize the data set generated by horizontal aggregation.
186.Integration of Sound Signature Authentication System.
Synopsis:

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This document provides guidelines for implementing anauthentication system which
works on graphical password and includes sound signature. Click based graphical
password provides security from brute force and dictionary attacks and they are not
predictive thus it’s not easy to breachthem and a sound signature is integrated along
with which enhances the security as this sound signature also under goes the password
verification, and once the graphical password along with the sound signature is
verified the user is allowed to log into the system.
187. Scalable Scheduling of Updates in Streaming Data
Warehouses.
Synopsis:
We discuss update scheduling in streaming data warehouses, which combine the features
of traditional data warehouses and data stream systems. In our setting, external sources
push append-only data streams into the warehouse with a wide range of interarrival times.
While traditional data warehouses are typically refreshed during downtimes, streaming
warehouses are updated as new data arrive. We model the streaming warehouse update
problem as a scheduling problem, where jobs correspond to processes that load new data
into tables, and whose objective is to minimize data staleness over time (at time t, if a table
has been updated with information up to some earlier time r, its staleness is t minus r). We
then propose a scheduling framework that handles the complications encountered by a
stream warehouse: view hierarchies and priorities, data consistency, inability to preempt
updates, heterogeneity of update jobs caused by different interarrival times and data
volumes among different sources, and transient overload. A novel feature of our framework
is that scheduling decisions do not depend on properties of update jobs (such as
deadlines), but rather on the effect of update jobs on data staleness. Finally, we present a
suite of update scheduling algorithms and extensive simulation experiments to map out
factors which affect their performance.
188.Tweet Analysis for Real-Time Event Detection and
Earthquake Reporting System Development.
Synopsis:
Twitter has received much attention recently. An important characteristic of Twitter is its
real-time nature. We investigate the real-time interaction of events such as earthquakes in
Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a
target event, we devise a classifier of tweets based on features such as the keywords in a
tweet, the number of words, and their context. Subsequently, we produce a probabilistic
spatiotemporal model for the target event that can find the center of the event location. We

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regard each Twitter user as a sensor and apply particle filtering, which are widely used for
location estimation. The particle filter works better than other comparable methods for
estimating the locations of target events. As an application, we develop an earthquake
reporting system for use in Japan. Because of the numerous earthquakes and the large
number of Twitter users throughout the country, we can detect an earthquake with high
probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic
intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects
earthquakes promptly and notification is delivered much faster than JMA broadcast
announcements.
189.Organizing User Search Histories.
Synopsis:
Users are increasingly pursuing complex task-oriented goals on the web, such as making
travel arrangements, managing finances, or planning purchases. To this end, they usually
break down the tasks into a few codependent steps and issue multiple queries around these
steps repeatedly over long periods of time. To better support users in their long-term
information quests on the web, search engines keep track of their queries and clicks while
searching online. In this paper, we study the problem of organizing a user's historical
queries into groups in a dynamic and automated fashion. Automatically identifying query
groups is helpful for a number of different search engine components and applications, such
as query suggestions, result ranking, query alterations, sessionization, and collaborative
search. In our approach, we go beyond approaches that rely on textual similarity or time
thresholds, and we propose a more robust approach that leverages search query logs. We
experimentally study the performance of different techniques, and showcase their potential,
especially when combined together.
190.Knowledge-Based Interactive Postmining of Association
Rules Using Ontologies.
Synopsis:
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of
delivered rules. To overcome this drawback, several methods were proposed in the
literature such as itemset concise representations, redundancy reduction, and
postprocessing. However, being generally based on statistical information, most of these
methods do not guarantee that the extracted rules are interesting for the user. Thus, it is
crucial to help the decision-maker with an efficient postprocessing step in order to reduce
the number of rules. This paper proposes a new interactive approach to prune and filter
discovered rules. First, we propose to use ontologies in order to improve the integration of

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user knowledge in the postprocessing task. Second, we propose the Rule Schema
formalism extending the specification language proposed by Liu et al. for user expectations.
Furthermore, an interactive framework is designed to assist the user throughout the
analyzing task. Applying our new approach over voluminous sets of rules, we were able, by
integrating domain expert knowledge in the postprocessing step, to reduce the number of
rules to several dozens or less. Moreover, the quality of the filtered rules was validated by
the domain expert at various points in the interactive process.
191.Selecting Attributes for Sentiment Classification Using
Feature Relation Networks.
Synopsis:
A major concern when incorporating large sets of diverse n-gram features for sentiment
classification is the presence of noisy, irrelevant, and redundant attributes. These concerns
can often make it difficult to harness the augmented discriminatory potential of extended
feature sets. We propose a rule-based multivariate text feature selection method called
Feature Relation Network (FRN) that considers semantic information and also leverages the
syntactic relationships between n-gram features. FRN is intended to efficiently enable the
inclusion of extended sets of heterogeneous n-gram features for enhanced sentiment
classification. Experiments were conducted on three online review testbeds in comparison
with methods used in prior sentiment classification research. FRN outperformed the
comparison univariate, multivariate, and hybrid feature selection methods; it was able to
select attributes resulting in significantly better classification accuracy irrespective of the
feature subset sizes. Furthermore, by incorporating syntactic information about n-gram
relations, FRN is able to select features in a more computationally efficient manner than
many multivariate and hybrid techniques.
192.Outsourced Similarity Search on Metric Data Assets.
Synopsis:
This paper considers a cloud computing setting in which similarity querying of metric data is
outsourced to a service provider. The data is to be revealed only to trusted users, not to the
service provider or anyone else. Users query the server for the most similar data objects to
a query example. Outsourcing offers the data owner scalability and a low-initial investment.
The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable
(e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents
techniques that transform the data prior to supplying it to the service provider for similarity
queries on the transformed data. Our techniques provide interesting trade-offs between
query cost and accuracy. They are then further extended to offer an intuitive privacy

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guarantee. Empirical studies with real data demonstrate that the techniques are capable of
offering privacy while enabling efficient and accurate processing of similarity queries.
193.USHER: Improving Data Quality with Dynamic Forms.
Synopsis:
Data quality is a critical problem in modern databases. data-entry forms present the first and
arguably best opportunity for detecting and mitigating errors, but there has been little
research into automatic methods for improving data quality at entry time. In this paper, we
propose Usher, an end-to-end system for form design, entry, and data quality assurance.
Using previous form submissions, Usher learns a probabilistic model over the questions of
the form. Usher then applies this model at every step of the data-entry process to improve
data quality. Before entry, it induces a form layout that captures the most important data
values of a form instance as quickly as possible and reduces the complexity of error-prone
questions. During entry, it dynamically adapts the form to the values being entered by
providing real-time interface feedback, reasking questions with dubious responses, and
simplifying questions by reformulating them. After entry, it revisits question responses that it
deems likely to have been entered incorrectly by reasking the question or a reformulation
thereof. We evaluate these components of Usher using two real-world data sets. Our results
demonstrate that Usher can improve data quality considerably at a reduced cost when
compared to current practice.
194.Mining Web Graphs for Recommendations.
Synopsis:
As the exponential explosion of various contents generated on the Web, Recommendation
techniques have become increasingly indispensable. Innumerable different kinds of
recommendations are made on the Web every day, including movies, music, images, books
recommendations, query suggestions, tags recommendations, etc. No matter what types of
data sources are used for the recommendations, essentially these data sources can be
modeled in the form of various types of graphs. In this paper, aiming at providing a general
framework on mining Web graphs for recommendations, (1) we first propose a novel
diffusion method which propagates similarities between different nodes and generates
recommendations; (2) then we illustrate how to generalize different recommendation
problems into our graph diffusion framework. The proposed framework can be utilized in
many recommendation tasks on the World Wide Web, including query suggestions, tag
recommendations, expert finding, image recommendations, image annotations, etc. The
experimental analysis on large data sets shows the promising future of our work.
195.The World in a Nutshell Concise Range Queries.

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Synopsis:
With the advance of wireless communication technology, it is quite common for people to
view maps or get related services from the handheld devices, such as mobile phones and
PDAs. Range queries, as one of the most commonly used tools, are often posed by the
users to retrieve needful information from a spatial database. However, due to the limits of
communication bandwidth and hardware power of handheld devices, displaying all the
results of a range query on a handheld device is neither communication-efficient nor
informative to the users. This is simply because that there are often too many results
returned from a range query. In view of this problem, we present a novel idea that a concise
representation of a specified size for the range query results, while incurring minimal
information loss, shall be computed and returned to the user. Such a concise range query
not only reduces communication costs, but also offers better usability to the users, providing
an opportunity for interactive exploration. The usefulness of the concise range queries is
confirmed by comparing it with other possible alternatives, such as sampling and clustering.
Unfortunately, we prove that finding the optimal representation with minimum information
loss is an NP-hard problem. Therefore, we propose several effective and nontrivial
algorithms to find a good approximate result. Extensive experiments on real-world data
have demonstrated the effectiveness and efficiency of the proposed techniques.
196.Network Coding Based Privacy Preservation against
Traffic Analysis in Multi-hop Wireless Networks.
Synopsis:
Privacy threat is one of the critical issues in multi-hop wireless networks, where attacks
such as traffic analysis and flow tracing can be easily launched by a malicious adversary
due to the open wireless medium. Network coding has the potential to thwart these attacks
since the coding/mixing operation is encouraged at intermediate nodes. However, the
simple deployment of network coding cannot achieve the goal once enough packets are
collected by the adversaries. On the other hand, the coding/mixing nature precludes the
feasibility of employing the existing privacy-preserving techniques, such as Onion Routing.
In this paper, we propose a novel network coding based privacy-preserving scheme against
traffic analysis in multi-hop wireless networks. With homomorphic encryption on Global
Encoding Vectors (GEVs), the proposed scheme offers two significant privacy-preserving
features, packet flow untraceability and message content confidentiality, for efficiently
thwarting the traffic analysis attacks. Moreover, the proposed scheme keeps the random
coding feature, and each sink can recover the source packets by inverting the GEVs with a
very high probability. Theoretical analysis and simulative evaluation demonstrate the validity
and efficiency of the proposed scheme.

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197.Query Planning for Continuous Aggregation Queries
over a Network of Data Aggregators.
Synopsis:
Continuous queries are used to monitor changes to time varying data and to provide results
useful for online decision making. Typically a user desires to obtain the value of some
aggregation function over distributed data items, for example, to know value of portfolio for
a client; or the AVG of temperatures sensed by a set of sensors. In these queries a client
specifies a coherency requirement as part of the query. We present a low-cost, scalable
technique to answer continuous aggregation queries using a network of aggregators of
dynamic data items. In such a network of data aggregators, each data aggregator serves a
set of data items at specific coherencies. Just as various fragments of a dynamic webpage
are served by one or more nodes of a content distribution network, our technique involves
decomposing a client query into subqueries and executing subqueries on judiciously chosen
data aggregators with their individual subquery incoherency bounds. We provide a
technique for getting the optimal set of subqueries with their incoherency bounds which
satisfies client query's coherency requirement with least number of refresh messages sent
from aggregators to the client. For estimating the number of refresh messages, we build a
query cost model which can be used to estimate the number of messages required to
satisfy the client specified incoherency bound. Performance results using real-world traces
show that our cost-based query planning leads to queries being executed using less than
one third the number of messages required by existing schemes.
198.Ranking Model Adaptation for Domain-Speciﬁc
Search.
Synopsis:
With the explosive emergence of vertical search domains, applying the broad-based ranking
model directly to different domains is no longer desirable due to domain differences, while
building a unique ranking model for each domain is both laborious for labeling data and time
consuming for training models. In this paper, we address these difficulties by proposing a
regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we
can adapt an existing ranking model to a new domain, so that the amount of labeled data
and the training cost is reduced while the performance is still guaranteed. Our algorithm
only requires the prediction from the existing ranking models, rather than their internal
representations or the data from auxiliary domains. In addition, we assume that documents
similar in the domain-specific feature space should have consistent rankings, and add some
constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking
adaptability measurement is proposed to quantitatively estimate if an existing ranking model

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can be adapted to a new domain. Experiments performed over Letor and two large scale
data sets crawled from a commercial search engine demonstrate the applicabilities of the
proposed ranking adaptation algorithms and the ranking adaptability measurement.
199.SCALABLE LEARNING OF COLLECTIVE
BEHAVIOUR .
Synopsis:
This study of collective behavior is to understand how individuals behave in a social
networking environment. Oceans of data generated by social media like Facebook, Twitter,
Flickr, and YouTube present opportunities and challenges to study collective behavior on a
large scale. In this work, we aim to learn to predict collective behavior in social media. In
particular, given information about some individuals, how can we infer the behavior of
unobserved individuals in the same network? A social-dimension-based approach has been
shown effective in addressing the heterogeneity of connections presented in social media.
However, the networks in social media are normally of colossal size, involving hundreds of
thousands of actors. The scale of these networks entails scalable learning of models for
collective behavior prediction. To address the scalability issue, we propose an edge-centric
clustering scheme to extract sparse social dimensions. With sparse social dimensions, the
proposed approach can efficiently handle networks of millions of actors while demonstrating
a comparable prediction performance to other nonscalable methods.
200.Scalable Scheduling of Updates in Streaming Data
Warehouses.
Synopsis:
We discuss update scheduling in streaming data warehouses, which combine the features
of traditional data warehouses and data stream systems. In our setting, external sources
push append-only data streams into the warehouse with a wide range of interarrival times.
While traditional data warehouses are typically refreshed during downtimes, streaming
warehouses are updated as new data arrive. We model the streaming warehouse update
problem as a scheduling problem, where jobs correspond to processes that load new data
into tables, and whose objective is to minimize data staleness over time (at time t, if a table
has been updated with information up to some earlier time r, its staleness is t minus r). We
then propose a scheduling framework that handles the complications encountered by a
stream warehouse: view hierarchies and priorities, data consistency, inability to preempt
updates, heterogeneity of update jobs caused by different interarrival times and data
volumes among different sources, and transient overload. A novel feature of our framework
is that scheduling decisions do not depend on properties of update jobs (such as

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deadlines), but rather on the effect of update jobs on data staleness. Finally, we present a
suite of update scheduling algorithms and extensive simulation experiments to map out
factors which affect their performance.

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