alexa Abstract | Horizontal Aggregations in SQL to Generate Query Sets for Data Mining and OLAP Cube Exploration
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access


Data mining is the domain which has utility in real world applications. Data sets are prepared from regular transactional databases for the purpose of data mining. A huge amount of time is needed for making the dataset for the data mining analysis because data mining practitioners required to write complex SQL queries and many tables are to be joined to get the aggregated result. We recommend simple, however powerful, techniques to generate SQL code to return aggregated columns in a very horizontal tabular page layout, returning a few numbers as opposed to one variety per short period. This new class involving functions is named 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. This paper focuses on building user-defined horizontal aggregations such as PIVOT, SPJ (SELECT PROJECT JOIN) and CASE whose underlying logic uses SQL queries, which prepares a query set consists of procedures and this query set will be stored in the database. One can easily access the query set and get the desired output to analyze the datasets. The queries which are not available in the query set can be generated dynamically using dynamic query generation. Finally we will see the experimental evaluation of data sets using SPJ and CASE method.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Rekha S. Nyaykhor , Nilesh T. Deotale

Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version