Query optimization in oodbms identifying subquery for query management

International Journal of Database Management Systems ( IJDMS ) Vol.6, No.2, April 2014
DOI : 10.5121/ijdms.2014.6204 49
QUERY OPTIMIZATION IN OODBMS:
IDENTIFYING SUBQUERY FOR QUERY
MANAGEMENT
Sheetal S. Dhande1
& Dr. G. R. Bamnote2
1
Department of Computer Engineering, Sipna’s College of Engg & Tech, Amravati,
Maharashtra, India
2
Prof & HOD Computer Science &Engineering Prof. Ram Meghe. Institute of Research,
Badnera Amravati, Maharashtra, India
ABSTRACT
This paper is based on relatively newer approach for query optimization in object databases, which uses
query decomposition and cached query results to improve execution a query. Issues that are focused here is
fast retrieval and high reuse of cached queries, Decompose Query into Sub query, Decomposition of
complex queries into smaller for fast retrieval of result.
Here we try to address another open area of query caching like handling wider queries. By using some
parts of cached results helpful for answering other queries (wider Queries) and combining many cached
queries while producing the result.
Multiple experiments were performed to prove the productivity of this newer way of optimizing a query.
The limitation of this technique is that it’s useful especially in scenarios where data manipulation rate is
very low as compared to data retrieval rate.
KEYWORDS
Query Caching, Query Decomposition, Query Optimization, Stack Based Approach (SBA), Stack-Based
Query Language (SBQL), Object Databases.
1. INTRODUCTION
Object-Oriented database technology is a marriage of object-oriented programming and database
technologies. How these programming and database concepts have come together to provide
what we now call object-oriented databases. Perhaps the most significant characteristic of object-
oriented database technology is that it combines object-oriented programming with database
technology to provide an integrated application development system. There are many advantages,
including the definition of operations with the definition of data. First, the defined operations
apply ubiquitously and are not dependent on the particular database application running at the
moment. Second, the data types can be extended to support complex data such as multi-media by
defining new object classes that have operations to support the new kind of information.

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Query processing and its optimization are the two most popular areas of research in the database
community. Query processing is the sequence of actions that takes as input a query formulated in
the user language and delivers as result the data asked for [1]. Query processing involves query
transformation and query execution. Query transformation is the mapping of queries and query
results back and forth through the different levels of the DBMS. Query execution is the actual
data retrieval according to some access plan, i.e. a sequence of operations in the physical access
language [2].
DBMSs support high performance of query processing by using special data structures (e. g.
indexes, stacks) or by performing operations like I/O operation simultaneously. However, the
major potential of performance improvement can be achieved by special preprocessing of query
before execution .This preprocessing referred to as query optimization, is performed by special
module called query optimizer.[3]
The task of query optimization ideally is to find the best execution plan, i.e. the execution plan
that costs the least, according to some performance measure. Usually, one has to accept just
feasible execution plans, because the number of semantically equivalent plans is too large to
allow for enumerative search [4].
In various types of Database Systems (Relational as well as Object-Oriented), many techniques
for query optimization are available [5]. Few of them are Pipelining, Parallel Execution,
Partitioning, Indexes, Materialized Views, and Hints etc [6] [7].
One technique which has not been convincingly implemented is Query Caching [8].
Query Caching will provide optimum performance. Instead of spending time re-evaluating the
query, the database can directly fetch the results from already stored cache. The most obvious
benefit of Query Caching can be seen in systems where Data Retrieval rate is very high when
compared to Data Manipulation. Hence database i.e. data store get modified after the long
periodic intervals. During these intervals if a particular [25]
2. RELATED WORK
The task of a Database Management System (DBMS) is to safely store usually large amounts of
consistent data and to provide easy and fast access to these data, either for retrieval or update
purposes. The data model of a DBMS provides possible structure of the data so that it provides
easy access to the user. The implementation of such a high-level query language requires an
enormous effort; it is the task of the query optimizer to ensure fast access to the data stored in the
database [1].
A DBMS is a complex piece of software that consists of many layers. The three main layers
distinguished are the user interface, an intermediate layer that is called the logical algebra, and
the bottom layer called the physical algebra, which provides mechanisms to actually access the
data stored in files.
[5][9]In query processing, the user query is first mapped into a logical algebra expression, and
this expression in turn is mapped into an expression of the physical algebra. Traditionally, query
optimization consists of two phases:

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• Logical optimization, which is the rewriting of a logical algebra expression into one that
(hopefully), can be evaluated with less cost.
• Cost-based optimization, which concerns the mapping of logical algebra expressions into
expressions of the physical algebra.
Object identity can be employed to speed up join processing, the presence of inheritance
hierarchies and path expressions allows to design new index structures.
New approach present in [8]for coupling OODBMSs (Object Oriented Database Management
Systems) and IRSs (Information Retrieval Systems) that provides enhanced flexibility and
functionality ,This approach allows to decide freely to which document collections, that are used
as retrieval context, document objects belong, which text contents they provide for retrieval and
how they derive their associated retrieval values, either directly from the retrieval machine or
from the values of related objects
Seen the success [8][9]that the query optimization knew in the relational model, in OODBMS
data are represented in the basis as of objects. Associations are implemented by the direct ties via
object identifying that permit a fast navigational access between the different objects.
[11]The usual cost measure especially for large databases is the total no of disk access (since
storage access is most costly operation) such number is determined among others by the size of
page which is an atomic unit of data that can be processed by a system (the most popular size of
page is 4-8 KB).For smaller databases, where most of the data involved in a query can be
completely stored in memory, the emphasis is usually on minimizing computation cost.
[12]While specifying in the class diagram a mono-valuated or multi-valuated tie toward another
class, queries can follow this ties in order to attend the searched objects. Specific features of
object oriented data models offer many additional opportunities for optimization.
Cost-based optimization [25] [26] is guided by specific database characteristics such as table size
(i.e. cardinality and tuple width), the presence of indices, etc. Usually, user languages are high-
level, declarative languages allowing stating what data should be retrieved, not how to retrieve
them. For each user query, many different execution plans exist, each having its own associated
costs. The task of query optimization ideally is to find the best execution plan, i.e. the execution
plan that costs the least, according to some performance measure.
From the conceptual point of [13][14] view transparency is the most essential property of a
cached query. Caching of query results yields relatively most improvement in a query evaluation
performance, i.e. significantly decreases the time of anticipation for a response from database
management system. Additional advantage of the query caching method is that reuse time of
cached results is independent of query type, its complexity and current database state. The
optimization method needs some time for storing in the cache queries, their results together with
proper structures for maintenance purposes.
[15] [16]. Database operations typically involve obtaining a database root from the OODBMS
which is usually a data structure like a graph, vector, hash table, or set and traversing it to obtain
objects to create, update or delete from the database. When a client requests an object from the
database, the object is transferred from the database into the application's cache where it can be
used either as a transient value that is disconnected from its representation in the database

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(updates to the cached object do not affect the object in the database) or it can be used as a mirror
of the version in the database in that updates to the object are reflected in the database and
changes to object in the database require that the object is refetched from the OODBMS.
[17] Accessing secondary storage is much more expensive than accessing main memory,
therefore some data ( partial query result) are stored in special main memory buffers.
[18] sometime function can be very expensive to execute, it is sometimes advantageous to cache
its result in case it is invoked multiple times with the same arguments. In such cases function
input and output are stored in a special data structure. If the function is invoked afterwards for
same arguments, it is enough to find the appropriate value in the structure , the subprogram does
not have to be executed again . there are three main methods to implements function caching.
Memorization: computation result are stored in main memory hash table.
Sorting: it is useful when an expensive function is to be executed for the value of relation
column; the input relation is stored on the input column and cache of only the last input/output
pair for the function is maintained
Hybrid cache: the basic idea is to be memorization , but to manage the input stream so that main
memory hash table never exceeds a maximum size; this is accomplished by staging tuples with
previously unseen input value to disk and rescanning them later
Query is calculated only once i.e. for the first time and 999 times the stored result is reused [15].
Data Manipulation can invalidate the cache results because the inserted /modified /deleted data
can bring the difference between the cached results and the actual results. Hence regeneration of
the cached results will be required to restore the results back again to the useful state .
Oracle 11g database system offers this mechanism for SQL and PLSQL. Mysql database also
implemented query chaining where only full selected query texts together with the corresponding
result stored in cache. LINQ, Microsoft .NET environment also have this kind of facility [5][19].
Our research is focused on how cached queries transparent mechanism can be used in query
optimization, assuming no changes to syntax, semantics and pragmatics of query language itself
[16]. But there is not any result caching solution implemented in current commercial and non –
commercial object oriented database system [20].
The functionality of a currently development database programming methodology called ODRA
(Object Database for Rapid Application development) which works fully on the object oriented
principles. The database programming language is called SBQL (Stack based query
language).There are different optimization techniques, which are available in ODRA like [21]
• Query Optimization through Cached Queries for Object- Oriented Query Language
SBQL.
• Query Optimization by Result Caching in the Stack-Based Approach.
• Query Optimization by Rewriting Compound Weakly Dependent Subqueries.

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The most important concept of this work, Query caching have been implemented by constructing
a prototype [22] which suggested in future scope (methods for reusing of some parts of cached
results or results of many cached queries combined together.) in Query Optimization through
Cached Queries for Object- Oriented Query Language SBQL
The experimentation for optimization of query will be done based on queries written in Stack
based query language (SBQL) syntax which is designed and implemented using a stack-based
architecture (SBA) framework [23][24].The performance gains will be measured by comparing
the results against the performance of the identical queries written and executed in Prototype with
cached and without cached queries.
This paper organized as section 1 Introduction. Section 2 Related work about Query optimization
in object database. Sections 3 stack base Approach and section 4 describes description of
optimization strategies-Query caching and further reuse the result in cached by earlier execution.
Section 5 shows experimental findings and section 6 Discuss Experimental Result of cached
semantically mapped queries and concludes.
3. STACK-BASED APPROACH
Stack-Based Query Language (SBQL) is useful for the design and implementation of wide range
of database models [23]. SBQL is developed according to the Stack-Based Architecture (SBA), a
conceptual framework for developing object-oriented query and programming languages [24]. In
SBQL the data is stored in the form of persistent objects and the collection of data objects is
called as Object Store. Hence adding, deleting or updating information in Object-oriented
Databases is nothing but adding, deleting or updating the objects. Objects may contain other
objects (aggregation) or references to other objects. Hence the Object-Oriented Modeling
concepts of complex objects, associations between objects, classes, types, methods, inheritance,
dynamic roles, encapsulation, polymorphism, semi-structured data and other features are
employed in the creation of Object Store Models, a representation of the database in Object
Databases.
3.1 Query Language (SBQL)
SBQL permits the use of all well-known query operators such as selection, projection,
navigation, path expressions, join, quantifiers, etc. SBQL has special as and group as alias
operators, apart from binary operators [either of algebraic or non-algebraic type.
In the evaluation of SBQL queries two stack are in use namely ENVS (Environmental Stack) and
QRES (Result Stack). In the processing of algebraic operators ENVS is not required to be used
[24]. The examples of algebraic operators are Operators for arithmetic and string comparisons,
set-oriented operators, aggregate functions, auxiliary naming operators, Boolean operators, etc. In
contrast, processing non-algebraic operators involves operations on ENVS. The examples of non-
algebraic operators are selection (where), projection/navigation and path expressions (dot),
dependent join (join) etc.

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3.1.1 Distinction of SBQL Queries
In contrast to SQL and OQL, SBQL queries can be easily decomposed into subqueries, down to
atomic ones, connected by unary or binary operators. This property simplifies implementation. In
query optimization decomposed atomic queries along with query caching plays an important role.
3.2 Object Database Models
SBA assumes a family of object store models which are enumerated AS0, AS1, AS2 and AS31.
The simplest is AS0, which covers relational, nested-relational and XML-oriented databases.
AS0 assumes hierarchical objects with no limitations concerning the nesting of objects and
collections. AS0 also covers pointer links (relationships) between objects.
4. QUERY OPTIMIZATION ARCHITECTURE
Trasform query into syntatic tree
.
Static evaluator and type checker , whether
query
syntatically correct , validate table name ,
coloum name,
operator , object name , method name , function
name involed in query.
According to rules of normalization and send
query
to query optimizer (which is based on chche
query
optimizer)
Interpret query , find result.
Find and dispaly query result.
4.1 Query Optimization
The scenario of the optimization using cached queries in query evaluation environment for SBA
is as follows [10].

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1) A user submits a query to a client-side user interface.
2) The user interface system passes it to the parser. The parser receives it and transforms
into a syntactic tree
3) The query syntax tree is then received by static evaluator and type checker. It checks
whether the query is syntactically correct or not. If not, it will report the errors. It also
validates the table names, column names, operators, procedure names, function
parameters involved query. Hence it will check the query’s semantics. For this purpose, it
will use the Metabase present on the server side. Metabase is a part of the database
system which contains the Meta information related with the data in the various objects.
This is static evaluation of the various nodes in the query syntax tree [12].
4) This type checked and statically evaluated query tree is then sent to the query normaliser
which reconstructs the query according to the rules of normalization. This normalised
query is then send to the query optimizer. All these components query parser, query type
checker; query normaliser, query optimizer and query interpreter are employed on the
client side database system.
5) The query optimizer rewrites the received normalized query using particular strategies
like query decomposition. Each decomposed part of the complex query is send to the
server [15]. Server checks whether the received sub query is already cached or not. If sub
query is present in cache, the Unique Identification number of the entry in cache which
corresponds to the result of the given sub query is dispatched to the query optimizer.
Optimizer replaces (rewrites) the sub query tree of the total query tree by a node
containing that unique identification number [16]. This UIN will be used by query
interpreter to directly fetch the result from the server. Hence all the parts of the query
whose results are already stored in cache will get replaced by their respective UIN. Due
to this all sub queries which get replaced by corresponding UIN, their results will be
brought from the cache & hence their re-evaluation will be avoided. This rewriting will
generate the best evaluation plan which promises to give the best performance & having
a least cost in terms of time and storage.
6) The optimized query evaluation plan is then sent to query interpreter.
7) The plan is executed by the query interpreter.
4.2 Class Diagram of Example Database
To implement this prototype we have design Match database. The additional information
included is as follows
• Information about officials involved in a test match will be included in the database.
These people include umpire, TV umpire, match referee and reserve umpire.
• To illustrate inheritance, here we created class person. All people participating in test
match will inherit properties from object person.

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Figure 2: Class Diagram of Match Database
4.3 Query Caching
Once the optimised evaluation plan is executed successfully, the query is cached on the server
side in pair <query, result>. Following that the calculated result of the query is send to the client
user who has submitted the query.
When the semantically equivalent query (written in the same or different way) is submitted by the
same or other user, after parsing, type checking and normalization of the query, optimizer sends
the query to the server side query cache manager. Query cache manager searches for the query in
the query cache registry and if found there will return the unique identification number (UIN) of
the corresponding result to the client, thus avoiding the recalculation of previously stored result.
Using this UIN, query interpreter (on the client system) can fetch the stored result of the query
directly from the server. If the query is not found in query cache registry, query cache manager

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will send a message to an optimizer (on the client side database system) indicating that a query is
not cached and hence its result needs to be calculated. Optimizer then does not rewrite the query
i.e. does not reconstruct a parse tree. That part of the query will be then calculated by the query
interpreter at runtime using runtime ENVS (Environmental Stack) and runtime QRES (Result
Stack) [22] [24].
Description of few components on server side:
Query Cache Manager – This is a program running in the server, its job is to check the Query
Cache Registry and figure out if the query is cached. The Query Optimizer with pass normalized
query (or normalised inner sub-query) to the Query Cache Manager.
Query Cache Registry – This contains all the cached queries along with the results.
4.4 Query Normalization
To prevent from placing in the cache, queries with different textual forms but the same semantic
meaning (& hence also will generate the same result), several query text normalization methods
will be used. Hence if a query is already stored in the cached with its result, all semantically
equivalent queries will make reuse of the stored result, as all those queries will be mapped with
the already stored query (due to normalization) [9].
Examples of few techniques useful in the process of normalization are:
a) Ordering of operands
Sum and multiply operations are put before subtractions or divisions [23], i.e. an arithmetic
expression is transformed as follows:
p / q / r * s / t
is normalized to:
p * s / q / r / t
b) Unification of Auxiliary Names
Auxiliary name used here for as or group as operators are unified. , it is not root of syntactic tree,
in other case , it ii memorized query result, evaluated earlier by static evaluator.
Query Q:
( ( fname where country = “india” ) as f ) join (f.batting as h ) ) . f . Ftest, h.MatchId)
Is normalized to
( ( (fname where country = “india” ) as cache _aux1) join (cache _aux1.batting as cache_aux2
)) . cache _aux1. Ftest, cache_aux2.MatchId)
c) Ordering based on column names (in the order in which they appear in the object description).
d) Column names should be maintained to the left side of each operator .

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4.5 Query Decomposition and Rewriting
After normalization phase query is decomposed, if possible, into one or many simpler candidate
sub queries. Query decomposition is a useful mechanism to speed up evaluating a greater number
of new queries. If we materialize a small independent subquery instead of a whole complex
query, then the probability of reusing of its results is risen. In addition, a simple semantic of the
decomposed query reduces the costs of its updating. Each isolated subquery and finally a whole
query is independently analyzed in context of the set of cached queries defined in the query cache
registry and if it hasn’t yet cached, it becomes a new candidate for caching.
Decomposition of queries in our proposed approached based on operators in SBQL , that are
algebraic and non algebraic in categories.
Nature of operator decided query is dependent and independent, if query join with algebraic
operator , always consider independent subqueries . if query involves a non algebraic operator ,
then subquery evaluated in the context determined by another subquery involved query.
4.5.1 Operator that identifies subqueries
In SBQL queries are combined by operators , which is algebraic and non algebraic , the majority
of operators in sbql are algebraic . they include numerical comparisons , numerical operators ,
string comparisons, Boolean and, or, not , aggregate function , set ,bag sequence operators and
comparisons , the Cartesian product (denoted by comma), etc[24].
Algebraic Operator
The definitions of algebraic operator are follows
Let q1 ∆ q2 be a query formed of two binary algebraic operator ∆, evaluation of this operation is
as follows
Algo evaluatequery(query)
Begin
……
……
resultarray : array
Case query is q1 ∆ q2 ( * ∆ is algebraic operator)
Begin
Evaluate query q1 and push the result into result stack
Evaluate query q2 and push the result into result stack
Read the result of q2
Save the result of q2 in resultarray
Read the result q1
Save the result of q1 in resultarray+1

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Apply ∆ (temp ∆ temp+1) to the result of both the subqueries and save the final result in result
stack
End.
And another useful algebraic operators is defining of an auxiliary name n ( for the result of query
q). it assigns the name n to each row of the result table / result array returned by q
Following are semantics
Match
Return the result table/ result array
<i1> , <i2>
Then the query
Match as M
Return the result table/ result array
{<M(i1)>, <M(i2)>}
Group as
This names entire result of query. The semantics of this operator is as follows: if q returns a
resulttable /result array t , then query
q group as N
returns a result of N(t) .
Non algebraic operator
The evaluation of non algebraic operators (a selection , a dot, a dependent join, quantifiers, etc) is
more complicated. In algebraic operator, subqueries q1 and q2 occurring in query q1 ∆ q2 are
evaluated independently, then result make up the final query result. In non algebraic operator if
query q1 and query q2 involves non algebraic operator θ, then q2 is evaluated in the context
determined by q1. This is the reason this operator are referred as non algebraic.
Algo eval(query);
Begin
.........
Case query is q1 θ q2; (* θ is non algebraic operator)
Begin
Partialresult : array of table;
Finalresult :tables; i: integer;
....
I :=1;
eval(q1) ; ( Evaluate q1 and store result in Result stack)
for each row r in top(resultstack) do

60
push( ) (* open new scope on Enviornment stack)
eval(q2) ; ( Evaluate q2 and store result in Result stack)
Partialresult[i]= combine (r, top(resultstack)); (* create partial result)
Pop() (* pop the current scope )
Pop(resultstack)
I=i+1;
End;
Finalresult= merage (Partialresult);
End; (* case)
End;( * eval)
The SBA semantics is uniform basis for the definition of several non algebraic operators of OQL
like language
Selection: q1 where q2 , where q1 is any query and q2 is Boolean valued query . If q2 returned
TRUE for the row r returned by q1, then r is the element of final result table; otherwise it is
skipped.
Navigation, Projection, path expression: q1.q2 the final result table is the union of tables
returned by q2 for each row r returned by q1. This construct covers generalized path expressions,
e.g. q1.q2.q3.q4 is understood as ((q1.q2).q3).q4
Dependent join , navigational join : q1σ q2 a partial result for particular r returned by q1 is a
table obtained by a concatenation of row r with each row returned by q2 for this r. the final result
is the union of partial results.
Quantifiers : q1 (q2) and q1(q2), where q1 is any query , and q2 is Boolean –valued query for
the final result is false for at least one row r returned by q1; otherwise the final result true . for
actual definition is applied., if q2 returns FLASE
4.5.2 Distributive Property for Identifying sub queries
Here considering non algebraic operators are distributive. The properties is defined below
Definition
A non algebraic operator θ is distributive, if any syntactically correct queries q1, q2, q3 (q1, q2
are union compatible) and for any database holds following
(q1 union q2) θ q3
Is semantically equivalent to
( q1 θ q3 ) union ( q2 θ q3)
In other world, a non algebraic operator θ is distributive , if for any query q1θ q2
Its result can be calculated as union of all result of r θ q2, where r is a row of the table returned by
q1.
That is, the final result is always a union of partial results taken for all rows returned by q1.

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Proposition I
Operator where, dot, and independent join are distributive
Union of partial result obtained for each row returned by the sub query occurring on the left side
of such an operator. Quantifiers are not distributive. There are also others non – algebraic
operators that are not distributive ( e.g. grouping, ordering) , not consider in this paper.
Proposition II
The following associativity properties hold
1. ( q1.q2).q3 is equivalent to q1.(q2.q3)
2. ( q1 ⋈ q2) ⋈ q3 is equivalent to q1 ⋈ ( q2 ⋈ q3)
3. (q1 ⋈ q2) . q3 is equivalent to q1.( q2.q3)
4. ( q1 ⋈ q2) where q3 is equivalent to q1 ⋈ ( q2 where q3)
Finding out subqueries
The concept of handing independent subqueries is investigate [] . in our approach we are finding
subqueries for handling wider and complex queries.( we called it as complex query , because it
combines different queries with non algebraic operator). Handling subqueries with context of sub
query result.
Query Q1: Find MatchID of all matches in database in which Tendulkar batted.
This query need to use two classes as the player name is given only in the class Player and batting
performance is given only in the lass batting . we will use a subquery to obtain the PID of
Tendulkar and use that information to obtain matched of matches that he has played in.
This query is simple use of a subquery. The subquery only return a single constant which is
compared with another value in the WHERE clause. If the comparison’s result is true then the
row in the outside query is selected otherwise it is not. In a query like this in which the subquery
returns only value, PID IN could be replaced by PID = but if are not certain that the result of the
subquery will be constant, then it is best to use the IN operator.
Batting where PID IN( (player where Lname= “Tendulkar”).playerID).matchID
In this query, we find subquery
( player where Lname= “Tendulkar”).playerID group as P
And transform whole query to following form
( ( ( ( player where Lname= “Tendulkar”).playerID) group as P ). (Batting where PID IN
.P).matchID)
We decompose query by identifying subqueries , if subquery is matched and proposed as cached
query uniquely identified by its reference id ,( reference id , which we created to identify cache
result stored in cache to identify query)
Batting where PID IN( $cache(reference_id#1).matchID)

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Figure 3: Sample Query Optimization
Using aggregate Function
Query Q2: Find maximum score of Tendulkar and MatchId of all matches in database in which
Tendulkar batted.
This query need to use two classes as the player name is given only in the class Player and batting
performance is given only in the class batting . it is same query as above but here we need to find
Maximum score of Tendulkar , here we write query by using aggregate function Max on attribute
Nruns .we will use a subquery to obtain the PID of Tendulkar and use that information to obtain
matched of matches that he has played in.
This query is simple use of a subquery. The subquery only return a single constant which is
compared with another value in the WHERE clause. If the comparison’s result is true then the
row in the outside query is selected otherwise it is not. In a query like this in which the subquery
returns only value, PID IN could be replaced by PID = but if are not certain that the result of the
subquery will be constant, then it is best to use the IN operator.
Batting where PID IN( (player where Lname= “Tendulkar”).playerID).matchID,Max( Nruns)
In this query , we find subquery
Qsub1: ( player where Lname= “Tendulkar”).playerID group as P
and
Qsub2: max( Bats.Batting.Nruns) group as q
And transform whole query to following form
( ( ( player where Lname= “Tendulkar”).playerID) group as P ).((Batting where PID IN .P ).
matched , batted.player.q )

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We decompose query by identifying subqueries , if subquery is matched and proposed as cached
query uniquely identified by its reference id ,( reference id , which we created to identify cache
result stored in cache to identify query)
Batting where PID IN
( $cache(reference_id#1).matchID,( batted .player($cache(reference_id#2))
Figure 4 : Sample of Query Optimization , Handling complex queries through Cache Reference.
Subquery is matched and proposed as cached query uniquely identified by its reference id ,
(reference id , which we created to identify cache result stored in cache to identify query)
( $cache(reference_id#1).matchID,( batted .player($cache(reference_id#2))
Figure 5: Sample Query Optimization through Reference Cache

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5. EXPERIMENTAL RESULT
We have the performance of the optimizer by calculating response times 100 subsequent results
using set of queries retrieving data from database containing over 15000 objects. We have
compare data with three optimization strategies with cache, without cache, with Db40, and in
many cases especially complex queries, wider queries response time were 100 times faster.
Table1 shows result in term of computation time (in micro second) of three different approaches
of three different complex queries ( considering complex queries , which contain many join
operation , aggregate functions and many subqueries)
6. CONCLUSION AND FUTURE SCOPE
Based on the experimental results we can state that Decomposition and Caching techniques in
Object Oriented Queries have resulted in approx around hundred percent increases in
performance and query output.
We have presented a new approach of query optimization, where query execution done using
caching of result of previously answered queries; here we try to address future scope of [17].
Issue that we are try to handle is recognizing some parts of cached result helpful for answering
other queries and combining many cached queries while producing a result of one wider query.
The work on cached queries is continued. There are many future extensions of this work, some
are extending caching solutions for distributed environment.
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66
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AUTHORS
S. S. Dhande is currentaly working as Associate. Prof. at Sipna college of Engineering
and Technology, Amravati . Maharastra, india. She did her B.E. ( comp sci and Engg)
and M.E. ( Comp Sci and Engg) from S. G. B , Amravati University, she is currently
pursuing her PhD in faculty of Engineering & Technology in area of Object Oriented
Database Management system. She has published many papers at national as well as
international level. She is member of ISTE, IE,IETE, India.
Dr. G. R. Bamnote is Professor and Head of Department of Computer Science and
Engineering at PRM Institute of Research and Technology and Dean of Engineering
faculty of Amravati university. He did BE from karad, pune university and ME, PhD
from Amravati university in 2009. He has published many papers at national as well
as international level and guided many students for PG and Phd. He is life member of
ISTE, CSI and fellow of IETE and IE of India.

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