IRJET- Missing Value Evaluation in SQL Queries: A Survey

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 162
Missing Value Evaluation in SQL Queries: A Survey
Smruti Mule1, Antara Bhattacharya2
1Student, Department Of Computer Science and Engineering ,G.H.Raisoni Institute of Engineering and Technology,
Nagpur, India, smrutimule@gmail.com
2 Assistant Professor, Dept. of Computer Science and Engineering,G.H.Raisoni Institute of Engineering and
Technology, Nagpur ,India ,antara.bhattacharya@raisoni.net
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Abstract - After decades have been passed of taking efforts
on performance of database, the usability and quality of
database systems have gained more importance in recent
years. However, answering to why-notquestionsi.eevaluating
missing answers in SQL Queries after doing a lot of work has
also gained more attention. The main goal of this research
paper is evaluating missing value in the results obtained with
respect to different SQL Queries. At the same time, this
research paper fulfills the following goals: (i) surveying the
problem of evaluating the missing values i.e. why-not
questions in SQL queries; (ii) searching the techniques for
giving answers to such type of questions using different
numeric and non-numeric data and (iii) comparing those
efficient strategies. This research paper also gives attention
towards related work which were done so far .
Key Words: Missing answers, usability, SQL, top-k
1. INTRODUCTION
After decades, database community is taking efforts on
evaluation of missing values i.e answering to why-not
questions and comparing techniques used for evaluation of
this type of missing answers. The systems which are used
today are more efficient. However, these types of systems
are prominent in determining evaluation of management of
data and evaluation of query [2]. But to the same degree,
these systems are not suitable for end users. Now a day’s ,
users are expecting that systems should be easy to interact
and understand. It means, users are not agreed upon the
results obtained from such type of systems. Users are more
interested in knowing reasons for why the current set of
result does not match their expectation i.e. why current set
of objects are returned in the result by these systems. In
particular, users may interested in knowing the reason for
missing expected data object in result set and also
interested in knowing why unexpected data objects appear
in result received from the system. As a next step, users may
also find proper explanations for these types of questions.
Any system that can provide best explanations for type of
questions mentioned above can be very helpful for users to
better understand their information needs and also to make
system more transparent and interactive to users[3],[5]. At
present, traditional database systems are unable to provide
any kind of exploratory data analysis facilities to support
above types of why and why-not questions. The studies
focusing on improvement of database usability (e.g.,
keyword search [2], similar graph matching [4], and spatial
keywords[5] ), explaining the feature of missing tuples
which are not present in the result of query, are getting
more importance. A why-not question [1], [2] is being posed
when a user is interested in knowing why their expected
tuples are not present in query result. Recently users are
unable to sift directly in the set of data to examine “why-
not?”, due to the reason that interface of query i.e web forms
are restricted by the types of queries expressed by them.
When end users fires SQL query to get data from database
and ask “why-not?”and are unable to search the possible
ways fir getting explanation by means of query interface,
easily cause the situation where users does not use the tool
anymore. This would be the bad situation for database
developers who give their most of the time to develop such
applications. At the same time, supporting different aspects
of giving explanation for missing answers [1], knowledge of
algorithms which are based on query evaluation is required,
that is out of scope for most database developers. Recently,
community of databasestartedthe researchontechniquesto
evaluate missing answers. Out of this, recent works focuses
on giving answers to why-not questions. In this research
paper, answering both why and why-not questions are
addressed for numeric and non-numericdata presentinSQL
queries. In this research paper , aim is to evaluate missing
answers in SQL queries in terms of the above mentioned
aspects in different numeric and non-numericdata thathave
not been investigated by others. Rests of the sections of this
paper are as follows: Section 2 describes related work;
Section 3 presents comparison betweenstrategies;Section4
outlines future work; and Section 5 concludes our paper.
2. RELATED WORK
Previous studies [1],[4],[6],[7],[2],[3]and [5] have done
research on problems of evaluating missing answers in SQL
queries in terms of various different perspective. Xu et al.

explains to a user why their expected answers are not
present in the current set of objects in query result and
returns a refined query that includes expected missing
answers back to the result. Query refinement method [1] is
used which includes numeric attributes. Drawback of this
method is that it is not useful for non-numeric data. Islam et
al. explains the problem of evaluating missing answers in
matching of similar graph which are used for graph
databases. To address this problem, they have proposed an
approximate solution approach as computing the exact
solution is NPhard [4]. The search space for the new query
graph is also established.Drawback isonlysuitableforgraph
databases. He et al. explains the problem of answering why-
not question on two types of top-k queries: the basic top-k
query where the users need tospecifythesetofweighting[6]
and the top-k dominating query where users do not need to
specify the set of weightings as therankingfunction ranksan
object higher if it dominate more objects. Drawback is not
suitable for non-numeric data. Saiful et al. proposes
technique that aims at evaluating the why-not questions in
queries which are reverse skyline [7]. Also technique to
explain modification of why-not and query point which
includes why-not point in reverse skyline of the point called
as query point. It also explains position of query point
anywhere in a region without disturbing existingpointsthat
are reverse skyline. Drawback is only suitable for points of
data whose dynamic skyline contains query points. Chen et
al. proposed that keywords which are special in top-k
queries retrieves the k objects which are best as per the
function which considers both distance which is called a
special distance and similarity of text. Algorithm which is
having optimization sets that performs sequential
examination of setsofcandidatekeywordsisdeveloped.Also
index-based bound-and-prune algorithm [2] is used.
Drawback is only suitable for initial set of query keywords.
Geo et al. define and offer solutions to why-not questions on
MPRQ [3]. He have given a proposal of a framework which
are having three solutions that are efficient as follows : one
which involves modification of original query, one which
involves modification of why-not set, and last that involves
both modification of original query and why-not set. Time
required is more as experiments are performed using data
sets which are synthetic and original. Chen et al. addresses
problem of evaluating the missing answers in terms of
keywords top- k queries by performing the refinement of
keywords which are original that provides user with those
keywords which explains their intention of query. Also the
algorithm having different optimized techniques [5] is
proposed that searches the better solutionwhichisbased on
sets of keyword tested one by one. In some cases,
identification of keywords becomes difficult for users.
3. COMPARISON BETWEEN DIFFERENT
STRATEGIES
SR.NO STRATEGY ADVANTAGE DISADVANTAGE
1 Query
refinement
method
Used for
finding missing
values which
include
numeric
attributes
Not suitable
for non-numeric
data.
2 Index-based
bound-and-
prune
algorithm
Evaluate the
sequence of
keywords
sequentially.
Only suitable for
examination of
keywords present
in query.
3 Metric
probabilistic
range
queries
Define and
offer solutions
to why-
not questions o
n MPRQ.
Time required is
more as
experiments are
performed using
both real and
synthetic data sets.
4 NPhard Explains the
problem of
evaluating
missing values
in matching
similar graph.
Only suitable for
graph databases.
5 Optimizatio
n
Techniques
Determinesthe
good solution
totally based
on keywords
which are
tested at once.
Identification of
exact keywords is a
difficult task for the
users.

6 Ranking
Function
address the
problem of
answering
why-not
questions on
two types of
top-k queries:
the top-k
dominating
queries and
basic top-k
query.
Not suitable for
non-numeric data.
7 Queries
called as
Reverse
skyline.
Describes how
to update the
points calledas
why-not points
and also the
query point.
Only suitable for
points of data
whose dynamic
skylines contains
query points.
4. FUTURE WORK
The problem of evaluating missing answers in SQL Quires i.e
answering to why and why-not questions in other data
settings including social networks are studying currently.In
particular, working is going on the following type of queries
to answer the why and why-not questions.
Social and Graph Queries: Due to the emerging websites of
social networking and their greater impact on our daily life,
there is urgency for social queries on such networks. Social
networks data are generally represented as graphs in
databases. Many websites of social networking develop
recommendation that are automatic over different items
like giving suggestions on making new friends, events etc.
Hence, the feedback for such automated recommendation is
of more importance if user is not satisfied with them always.
Any social networking websites thatcananswersuchtypeof
why and why-not questions will be more interesting to their
users. In future work, this issue can be studied on queries
including data types like Binary Large Object (BLOB),
Boolean and others for missing value restoration and
thereby making the system flexible for maximum databases.
5.CONCLUSION
This paper presents the research agendas for evaluating the
missing answers in SQL Queries. Also shown why it is worth
conducting such research and outlined the various
techniques of giving answers to such type of why-not
questions. These papers have also summarized the related
work done in this area and the future research agendas.
Finally, contributions made so far are presented. Currently
work is in progress and focusing on future research
problems mentioned in this paper.
ACKNOWLEDGEMENT
The Research presented in this paper is possible due to the
data which is available through various reference papers
mentioned in this research paper. I take this opportunity to
express my gratitude towards Asst. Prof. Ms. Antara
Bhattacharya forcontinuoussupportingandencouragement.
I wouldlike to thank our Head Of the DepartmentV.M.Sahare
for giving proper guidance to submit this paper.
REFERENCES
[1] Wenjian Xu, Zhian He, Eric Lo, and Chi-Yin Chow,
“Explaining MissingAnswers toTop-k SQLQueries,”
IEEE Trans. Knowl. Data Eng., vol. 28, no. 8, pp.
2071–2085, July 2016.
[2] Lei Chen , Jianliang Xu, Xin Lin, Christian S.Jensen
and Haibo Hu, “Answering why-not spatial
keyword top-k queries via keyword adaption,” in
Proc. IEEE 32nd Int. Conf. Data Eng.,2016, pp. 697-
708.
[3] Yunjun Gao, Kai Wang, Christian S. Jensen and Gang
Chen, “Answering why-not questions on metric
probabilistic range queries,” in Proc. IEEE 32nd Int.
Conf. Data Eng.,2016, pp. 767-778.
[4] M. S. Islam, C. Liu, and J. Li, “Efficient answering of
why-not questions in similar graph matching,”IEEE
Trans. Knowl. Data Eng., vol. 27, no. 10, pp. 2672–
2686, Oct. 2015.
[5] L. Chen, X. Lin, H. Hu, C. S. Jensen, and J. Xu,
“Answering why not questions on spatial keyword
top-k queries,” in Proc. IEEE 31st Int. Conf. Data
Eng. , 2015, pp. 279–290.
[6] Z. He and E. Lo, “Answering why-not questions on
top-k queries,” IEEE Trans. Knowl. Data Eng., vol.
26, no. 6, pp. 1300–1315, Jun 2014
[7] Z. He and E. Lo, “Answering why-not questions on
top-k queries,” IEEE Trans. Knowl. Data Eng., vol.
26, no. 6, pp. 13Md. Saiful, Z. Rui, and L. Chengfei,
“On answering why-not questionsin reverseskyline
queries,” in Proc. IEEE 28th Int. Conf. Data Eng.,
2013, pp. 973–984.

IRJET- Missing Value Evaluation in SQL Queries: A Survey

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IRJET- Missing Value Evaluation in SQL Queries: A Survey