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Nitin S.Kharat / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.119-122
119 | P a g e
Deciding the Correct Usage of Database Queries, Data Mining
and OLAP in an Applications
Nitin S.Kharat.
B.E. (I.T.) M.E.(Computer Engg.)
Department of Computer Engineering, University of Pune, Pune,Maharashtra,India.
ABSTRACT
In the recent years, numbers of the
studies have been done on different techniques of
information retrieval. These retrieval techniques
includes Database Queries, Data Mining and
Online Analytical Processing (OLAP).The
retrieved information is used for various
purposes according to the different
requirements. The retrieved information might
be used for the purpose of Analysis, for the
purpose of various users behavior prediction or
for the purpose of Decision Support System
(DSS).Now the confusion is that when to use
Database Queries, when to use Data Mining and
when to use Online Analytical Processing
(OLAP).This paper elaborates the usage of
Database queries, Data Mining, OLAP according
to the user’s purpose, requirements at the
particular instant. The paper also emphasized on
performance of these techniques with
appropriate examples. The goal of paper is to
give the better clue to the user about the usage of
techniques such as Database Queries, Data
Mining and OLAP in an application to get the
information in an easy way with efficient
performance.
Key Terms: Database Queries, Data Mining,
Decision Support System (DSS), Online Analytical
Processing (OLAP)
1. INTRODUCTION
In today’s world because of the lack of
time number of the people avoids to go through the
large volume of database. As a result the numbers of
various information retrieval techniques have been
developed. These information retrieval techniques
allows to retrieve the required ,interested
information from the large volume of database
within compact time and in an easy way that’s why
the number of people chooses these technique as a
source of information retrieval. Such a techniques
includes the Database Queries, Data Mining and
Online Analytical Processing (OLAP) .As we all
known these techniques plays an vital role in
business including Sales,Marketing,Finanace etc. for
the purpose of Sales forecasting, Marketing
Research and Finance Analysis. But the crucial skill
is that utilizing appropriate technique
at the appropriate time according to purpose,
requirement, and performance is an important
scenario.
This paper elaborates the usage of these
techniques according to the requirement, purpose,
and performance with appropriate examples in an
efficient manner.
The remaining of this paper is organized as
follows. Paper briefly review the related work in
section 2, in section 3 described the usage of each
technique according to purpose, requirement and
performance, the future work and conclusion in
section 4.
2. RELATED WORK
In the previous research several studies [1,
2, 3, 4] focused on the possibilities and combining
of OLAP and Data Mining.
And also H.Zua [5] proposed and developed
association rule mining with interesting pattern, this
approach is called as Online Analytical Mining of
Association Rule.
In paper [6] developed an approach that operates on
two factors support and confidence of users
demanded items. This technique identifies item sets
which are frequently accessed through the web
pages on web site by particular user. The output
contains list of customer associated with that
product.Dzeroski et al.[7] combines OLAP and
output which proved that CUBE File outperforms
as compared to the hierarchical clustering.
The paper [8 ] also proved that the efficiency and
capability of integrating data mining into OLAP and
OLAM systems .OLAM is an way for mining the
required data from large volume of
multidimensional database.
The paper [9] also gives an efficient way to convert
the XML schema to ROLAP which helps to speed
up the execution time which results in improved
performance of OLAP as compared to previous
approaches.
In the paper [10] targeted to extract the previously
unidentified information from large volume of
database and supported the speed up process by
automatic OLAP schema generation which
ultimately reduces the manual work and speed up
the performance.
Nitin S.Kharat / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.119-122
120 | P a g e
3. USAGE OF DIFFERENT TECHNIQUES
3.1 Database queries:
Basically Database Queries Composes of:
.Database table which is representation of
mathematical relation that is set of items that share
the certain attributes.
.Each table column represents an attribute of
relation
.In relational databases the tables are usually named
after kind of they represent.
.Database queries allows the user to efficiently and
effectively to manipulate a database.
3.1.1 When to Use:
.When the user needs to retrieve the data from
database table such a that the retrieved data must
satisfies certain criteria defined by user.
.Suitable to use for the application which gives
query response as a small volume of data.
.When the User wants to retrieve the set of data
items strictly fulfilling defined constraint.
.When the user needs a specific information or data.
3.1.2 Example:
Consider you have a following database:
Table 1: Customer Database
.If the user wants to retrieve the names whose
income is greater than 3 LK and city is Pune then
he should fire
Database Queries as follows:
. Select Name from customer where Income>3 LK
and City=”Pune”.
3.1.3 Performance:
.Gives better performance when there are several
numbers of the fulfilling criteria.
.Gives better performance when the retrieved sets of
items are small in volume.
.It gives better performance against the specific
retrieval of information as per user need.
.Gives better performance when user fires ad hoc
queries to virtually any instance in a database.
3.2 Data Mining
Basically Data Mining Composes of:
.Different models for the analysis purpose
.Models can be viewed as high level summarizing of
underlying data
.Includes the different forms such as:
-Decision Tree
-Rule for classification task
-Association Rule e.g. Market Basket Analysis
-Clustering for Market Segmentation etc.
3.2.1 When to Use:
.It can be used when user wants to generate high-
level, actionable summaries of data residing in
database tables.
.When the user wants to builds the Association
Rules.
.When the database is too large for analysis.
.When user need to analyze the database in visual
form.
.When the User wants to analyze dataset using more
simple form i.e. Models such as:
-Decision Tree
-Graphs
-Histogram
-Pie Chart etc.
3.2.2 Example:
Consider the same database:
Table 2: Customer Database
.Now here you can use Data Mining technique for
building the Association Rules for the Decision
Support System (DSS).
.The Association Rule for Income can be built as
follows:
If Age>21 and City=”Mumbai” then Income> 3 LK
OR
If Vehicle=”BMW” and Total>3 LK then
Income>3 LK
.These Association Rules are very simple to
understand for any user because it summarizes
number of records from Customer database but if
we use Database Queries then it will become a very
complicated to the user to understand the above
scenario.
3.2.3 Performance:
.Gives the better performance than the Database
Queries over the large number of database for
analysis.
.Easy to understand as there are no more complex
queries
. Association Rules gives analysis at a once glance.
.No need to analyze each and every query.
.Visual representation increases more effectiveness
for analysis.
3.3 OLAP
Nitin S.Kharat / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.119-122
121 | P a g e
Basically OLAP (Online Analytical Processing)
Composes of:
.Summarizing the data before it is possible to
execute the queries.
.Summarization can be represented as cubes and
subcubes .
.Cube is precalculated and preaggregated data
.It allows reporting data, visualizing data and
interaction with views of data.
.It uses the star schema, snowflake schema which
consists of fact table and dimension table.
3.3.1 When to Use:
.Used against the normalized set of database tables
to get
the set of items that fulfills the constraints on its
attribute value.
.When the user needs to be data collected from
different tables i.e. Complex Joins.
.To speeds up the execution time because it uses
ROLAP.
.When user needs summarized data from detailed
data i.e. roll up.
.When the needs detailed data from summarized
data i.e.
Drill Down.
.When the user wants methodology for organization
of databases along the dimensions of a business
making the database more comprehensible.
.When the user needs to compare and contrast
measures along the business dimension in real time.
3.3.2 Example:
Consider the same database:
OLAP constructs the Star Schema(see Fig 1) for
above database to speed up the execution time as
follows:
.The dimension tables give rise to the dimensions in
the pre-aggregated data cubes..The fact table relates
the dimensions to each other and specifies the
measures which are to be aggregated.
.Here the measures are “dollar_total”, “sales_tax”,
and shipping_charge”.
Fig 1: Star Schema of the Database
.Figure 2 shows a three-dimensional data cube pre-
aggregated from the star schema
.Data cubes can be seen as a compact representation
of pre-computed query results.
.Essentially, each segment in a data cube represents
a pre- computed query result to a particular query
within a given star schema.
.The efficiency of cube querying allows the user to
interactively move from one segment in the cube to
another enabling the inspection of query results in
real time.
.Cube querying also allows the user to group and
ungroup segments, as well as project segments
onto given dimensions.
.This corresponds to such OLAP operations as roll-
ups, drill-downs, and slice-and-dice, respectively.
.Cube is represented as follows:
Fig 2: 3-Dimensional Cube
3.3.3 Performance:
.Gives the better performance than Database queries
and Data mining when the data need to be collected
from different table’s i.e. over Complex Joins.
.Cube gives the better performance since segments
inside the cubes are precalculated and
preaggregated.
4. CONCLUSION
This paper covers the correct usage of
information retrieval techniques such as Database
Queries, Data Mining and OLAP according to users’
purpose, requirement and performance along with
examples. The paper also concluded that in
Database Queries ad hoc queries are fired by user to
access the specific information whereas in Data
Mining generally modeling is used to represent the
huge retrieved data while in OLAP, it allows the
user for real time gain to pre-aggregated measures
along with dimension for more effective analysis.
The future work can be enhanced in such a
way that one can integrate these different techniques
according to requirement, purpose and performance
which is absolutely right for the application. If he is
able to do so then it will lead to huge benefits to
their application as well as organization.
Nitin S.Kharat / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.119-122
122 | P a g e
REFERENCES
[1]. S. Goil and A. Choudhary, “High
performance OLAP and data mining on
parallel computers,” Data Mining and
Knowledge Discovery, vol. 1, no. 4, pp.
391-417, Dec. 1997.
[2]. S. Asghar, D. Alahakoon and A. Hsu,
“Enhancing OLAP functionality using
selforganizing neural networks,” Neural,
Parallel and Scientific Computations, vol.
12, no. 1, pp. 1-20, March 2004.
[3]. R. B. Messaoud, O. Boussaid and S.
Rabaseda, “A new OLAP aggregation
based on the AHC technique,” in Proc. of
the 7th ACM Int’l Workshop on Data
Warehousing and OLAP (DOLAP), ACM
New York, 2004, pp. 65-72.
[4]. J. Han, “Towards online analytical mining
in large databases,” ACM SIGMOD
Record, vol. 27, no. 1, pp. 97-107, March
1998
[5]. H. Zhu, “Online analytical mining of
association rules,” Master Thesis, Simon
Fraser University, 1998, pp. 1-117
[6]. J. Fong, H. K. Wong and A. Fong, “Online
analytical mining Webpages tick
sequences,” J. of Data Warehousing, vol.
5, no. 4, pp. 59-67, 2000
[7]. S. Dzeroski, D. Hristovski and B. Peterlin,
“Using data mining and OLAP to discover
patterns in a database of patients with Y
chromosome deletions,” in Proc. AMIA
Symp., 2000, pp. 215–219.
[8]. J. Seo, et. al., “Interactive color mosaic and
dendrogram displays for signal/noise
optimization in microarray data analysis,”
in Proc. of the Intl. Conf. on Multimedia
and Expo-Volume 3, 2003, pp. 461-464.
[9] Sarbani Dasgupta, Soumya Sen, Nabendu
Chaki; "A Framework To Convert XML
Schema to ROLAP"; Proc. of 2nd Intl.
Conf. on Emerging Applications of
Information Technology, 2011.
[10] Muhammad Usman, Sohail Asghar, Simon
Fong”Data Mining and Automatic OLAP
Schema Generation”.
[11] Soumya Sen, Ranak Ghosh, Debanjali
Paul, NabenduChaki; "Integrating Related
XML Data into MultipleData Warehouse
Schemas"; Proc. of the First International
Conference on Information Technology
Convergence and Services (ITCS 2012),
Bangalore, India.

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  • 1. Nitin S.Kharat / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.119-122 119 | P a g e Deciding the Correct Usage of Database Queries, Data Mining and OLAP in an Applications Nitin S.Kharat. B.E. (I.T.) M.E.(Computer Engg.) Department of Computer Engineering, University of Pune, Pune,Maharashtra,India. ABSTRACT In the recent years, numbers of the studies have been done on different techniques of information retrieval. These retrieval techniques includes Database Queries, Data Mining and Online Analytical Processing (OLAP).The retrieved information is used for various purposes according to the different requirements. The retrieved information might be used for the purpose of Analysis, for the purpose of various users behavior prediction or for the purpose of Decision Support System (DSS).Now the confusion is that when to use Database Queries, when to use Data Mining and when to use Online Analytical Processing (OLAP).This paper elaborates the usage of Database queries, Data Mining, OLAP according to the user’s purpose, requirements at the particular instant. The paper also emphasized on performance of these techniques with appropriate examples. The goal of paper is to give the better clue to the user about the usage of techniques such as Database Queries, Data Mining and OLAP in an application to get the information in an easy way with efficient performance. Key Terms: Database Queries, Data Mining, Decision Support System (DSS), Online Analytical Processing (OLAP) 1. INTRODUCTION In today’s world because of the lack of time number of the people avoids to go through the large volume of database. As a result the numbers of various information retrieval techniques have been developed. These information retrieval techniques allows to retrieve the required ,interested information from the large volume of database within compact time and in an easy way that’s why the number of people chooses these technique as a source of information retrieval. Such a techniques includes the Database Queries, Data Mining and Online Analytical Processing (OLAP) .As we all known these techniques plays an vital role in business including Sales,Marketing,Finanace etc. for the purpose of Sales forecasting, Marketing Research and Finance Analysis. But the crucial skill is that utilizing appropriate technique at the appropriate time according to purpose, requirement, and performance is an important scenario. This paper elaborates the usage of these techniques according to the requirement, purpose, and performance with appropriate examples in an efficient manner. The remaining of this paper is organized as follows. Paper briefly review the related work in section 2, in section 3 described the usage of each technique according to purpose, requirement and performance, the future work and conclusion in section 4. 2. RELATED WORK In the previous research several studies [1, 2, 3, 4] focused on the possibilities and combining of OLAP and Data Mining. And also H.Zua [5] proposed and developed association rule mining with interesting pattern, this approach is called as Online Analytical Mining of Association Rule. In paper [6] developed an approach that operates on two factors support and confidence of users demanded items. This technique identifies item sets which are frequently accessed through the web pages on web site by particular user. The output contains list of customer associated with that product.Dzeroski et al.[7] combines OLAP and output which proved that CUBE File outperforms as compared to the hierarchical clustering. The paper [8 ] also proved that the efficiency and capability of integrating data mining into OLAP and OLAM systems .OLAM is an way for mining the required data from large volume of multidimensional database. The paper [9] also gives an efficient way to convert the XML schema to ROLAP which helps to speed up the execution time which results in improved performance of OLAP as compared to previous approaches. In the paper [10] targeted to extract the previously unidentified information from large volume of database and supported the speed up process by automatic OLAP schema generation which ultimately reduces the manual work and speed up the performance.
  • 2. Nitin S.Kharat / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.119-122 120 | P a g e 3. USAGE OF DIFFERENT TECHNIQUES 3.1 Database queries: Basically Database Queries Composes of: .Database table which is representation of mathematical relation that is set of items that share the certain attributes. .Each table column represents an attribute of relation .In relational databases the tables are usually named after kind of they represent. .Database queries allows the user to efficiently and effectively to manipulate a database. 3.1.1 When to Use: .When the user needs to retrieve the data from database table such a that the retrieved data must satisfies certain criteria defined by user. .Suitable to use for the application which gives query response as a small volume of data. .When the User wants to retrieve the set of data items strictly fulfilling defined constraint. .When the user needs a specific information or data. 3.1.2 Example: Consider you have a following database: Table 1: Customer Database .If the user wants to retrieve the names whose income is greater than 3 LK and city is Pune then he should fire Database Queries as follows: . Select Name from customer where Income>3 LK and City=”Pune”. 3.1.3 Performance: .Gives better performance when there are several numbers of the fulfilling criteria. .Gives better performance when the retrieved sets of items are small in volume. .It gives better performance against the specific retrieval of information as per user need. .Gives better performance when user fires ad hoc queries to virtually any instance in a database. 3.2 Data Mining Basically Data Mining Composes of: .Different models for the analysis purpose .Models can be viewed as high level summarizing of underlying data .Includes the different forms such as: -Decision Tree -Rule for classification task -Association Rule e.g. Market Basket Analysis -Clustering for Market Segmentation etc. 3.2.1 When to Use: .It can be used when user wants to generate high- level, actionable summaries of data residing in database tables. .When the user wants to builds the Association Rules. .When the database is too large for analysis. .When user need to analyze the database in visual form. .When the User wants to analyze dataset using more simple form i.e. Models such as: -Decision Tree -Graphs -Histogram -Pie Chart etc. 3.2.2 Example: Consider the same database: Table 2: Customer Database .Now here you can use Data Mining technique for building the Association Rules for the Decision Support System (DSS). .The Association Rule for Income can be built as follows: If Age>21 and City=”Mumbai” then Income> 3 LK OR If Vehicle=”BMW” and Total>3 LK then Income>3 LK .These Association Rules are very simple to understand for any user because it summarizes number of records from Customer database but if we use Database Queries then it will become a very complicated to the user to understand the above scenario. 3.2.3 Performance: .Gives the better performance than the Database Queries over the large number of database for analysis. .Easy to understand as there are no more complex queries . Association Rules gives analysis at a once glance. .No need to analyze each and every query. .Visual representation increases more effectiveness for analysis. 3.3 OLAP
  • 3. Nitin S.Kharat / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.119-122 121 | P a g e Basically OLAP (Online Analytical Processing) Composes of: .Summarizing the data before it is possible to execute the queries. .Summarization can be represented as cubes and subcubes . .Cube is precalculated and preaggregated data .It allows reporting data, visualizing data and interaction with views of data. .It uses the star schema, snowflake schema which consists of fact table and dimension table. 3.3.1 When to Use: .Used against the normalized set of database tables to get the set of items that fulfills the constraints on its attribute value. .When the user needs to be data collected from different tables i.e. Complex Joins. .To speeds up the execution time because it uses ROLAP. .When user needs summarized data from detailed data i.e. roll up. .When the needs detailed data from summarized data i.e. Drill Down. .When the user wants methodology for organization of databases along the dimensions of a business making the database more comprehensible. .When the user needs to compare and contrast measures along the business dimension in real time. 3.3.2 Example: Consider the same database: OLAP constructs the Star Schema(see Fig 1) for above database to speed up the execution time as follows: .The dimension tables give rise to the dimensions in the pre-aggregated data cubes..The fact table relates the dimensions to each other and specifies the measures which are to be aggregated. .Here the measures are “dollar_total”, “sales_tax”, and shipping_charge”. Fig 1: Star Schema of the Database .Figure 2 shows a three-dimensional data cube pre- aggregated from the star schema .Data cubes can be seen as a compact representation of pre-computed query results. .Essentially, each segment in a data cube represents a pre- computed query result to a particular query within a given star schema. .The efficiency of cube querying allows the user to interactively move from one segment in the cube to another enabling the inspection of query results in real time. .Cube querying also allows the user to group and ungroup segments, as well as project segments onto given dimensions. .This corresponds to such OLAP operations as roll- ups, drill-downs, and slice-and-dice, respectively. .Cube is represented as follows: Fig 2: 3-Dimensional Cube 3.3.3 Performance: .Gives the better performance than Database queries and Data mining when the data need to be collected from different table’s i.e. over Complex Joins. .Cube gives the better performance since segments inside the cubes are precalculated and preaggregated. 4. CONCLUSION This paper covers the correct usage of information retrieval techniques such as Database Queries, Data Mining and OLAP according to users’ purpose, requirement and performance along with examples. The paper also concluded that in Database Queries ad hoc queries are fired by user to access the specific information whereas in Data Mining generally modeling is used to represent the huge retrieved data while in OLAP, it allows the user for real time gain to pre-aggregated measures along with dimension for more effective analysis. The future work can be enhanced in such a way that one can integrate these different techniques according to requirement, purpose and performance which is absolutely right for the application. If he is able to do so then it will lead to huge benefits to their application as well as organization.
  • 4. Nitin S.Kharat / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.119-122 122 | P a g e REFERENCES [1]. S. Goil and A. Choudhary, “High performance OLAP and data mining on parallel computers,” Data Mining and Knowledge Discovery, vol. 1, no. 4, pp. 391-417, Dec. 1997. [2]. S. Asghar, D. Alahakoon and A. Hsu, “Enhancing OLAP functionality using selforganizing neural networks,” Neural, Parallel and Scientific Computations, vol. 12, no. 1, pp. 1-20, March 2004. [3]. R. B. Messaoud, O. Boussaid and S. Rabaseda, “A new OLAP aggregation based on the AHC technique,” in Proc. of the 7th ACM Int’l Workshop on Data Warehousing and OLAP (DOLAP), ACM New York, 2004, pp. 65-72. [4]. J. Han, “Towards online analytical mining in large databases,” ACM SIGMOD Record, vol. 27, no. 1, pp. 97-107, March 1998 [5]. H. Zhu, “Online analytical mining of association rules,” Master Thesis, Simon Fraser University, 1998, pp. 1-117 [6]. J. Fong, H. K. Wong and A. Fong, “Online analytical mining Webpages tick sequences,” J. of Data Warehousing, vol. 5, no. 4, pp. 59-67, 2000 [7]. S. Dzeroski, D. Hristovski and B. Peterlin, “Using data mining and OLAP to discover patterns in a database of patients with Y chromosome deletions,” in Proc. AMIA Symp., 2000, pp. 215–219. [8]. J. Seo, et. al., “Interactive color mosaic and dendrogram displays for signal/noise optimization in microarray data analysis,” in Proc. of the Intl. Conf. on Multimedia and Expo-Volume 3, 2003, pp. 461-464. [9] Sarbani Dasgupta, Soumya Sen, Nabendu Chaki; "A Framework To Convert XML Schema to ROLAP"; Proc. of 2nd Intl. Conf. on Emerging Applications of Information Technology, 2011. [10] Muhammad Usman, Sohail Asghar, Simon Fong”Data Mining and Automatic OLAP Schema Generation”. [11] Soumya Sen, Ranak Ghosh, Debanjali Paul, NabenduChaki; "Integrating Related XML Data into MultipleData Warehouse Schemas"; Proc. of the First International Conference on Information Technology Convergence and Services (ITCS 2012), Bangalore, India.