RuleGen – A Rule Generation Application Using Multiset Decision Tables

Bibliographic Details
Main Author:	Gundavarapu, Madhavi
Language:	English
Published:	University of Akron / OhioLINK 2005
Subjects:	Computer Science Learning Rules LERS-M
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=akron1140111266

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-akron11401112662021-08-03T05:24:40Z RuleGen – A Rule Generation Application Using Multiset Decision Tables Gundavarapu, Madhavi Computer Science Learning Rules LERS-M Volumes of data are generated constantly by several computer applications running across the globe. This trend of rapid increase in data generation is triggered by the ever-increasing usage of internet and database applications. In any particular application domain, having a large database that represents an equally large collection of facts about the domain, is of little use to experts trying to analyze facts, recognize trends or patterns and make decisions. Extracting useful information and ironing out inconsistencies from raw data is a challenging task and draws the research interests of computer scientists globally. This study presents a system called RuleGen, for generating rules from data tables stored in databases. It is implemented using SQL (Structured Query Language), a standard language for developing database applications. The underlying algorithm is based on learning program LERS-M (Learning from Examples based on Rough MultiSets). RuleGen provides capability to transform numerical data in data tables into a format that can be easily understood and interpreted by users. In addition, users can define any number of views for a particular database table and choose to materialize a view at the time of definition or at a later point in time. In each view, users can define a different set of condition and/or decision attributes. This provides the users the capability to analyze a single table from different perspectives. This is similar to the slice and dice capability provided by OLAP tools. 2005 English text University of Akron / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=akron1140111266 http://rave.ohiolink.edu/etdc/view?acc_num=akron1140111266 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science Learning Rules LERS-M
spellingShingle	Computer Science Learning Rules LERS-M Gundavarapu, Madhavi RuleGen – A Rule Generation Application Using Multiset Decision Tables
author	Gundavarapu, Madhavi
author_facet	Gundavarapu, Madhavi
author_sort	Gundavarapu, Madhavi
title	RuleGen – A Rule Generation Application Using Multiset Decision Tables
title_short	RuleGen – A Rule Generation Application Using Multiset Decision Tables
title_full	RuleGen – A Rule Generation Application Using Multiset Decision Tables
title_fullStr	RuleGen – A Rule Generation Application Using Multiset Decision Tables
title_full_unstemmed	RuleGen – A Rule Generation Application Using Multiset Decision Tables
title_sort	rulegen – a rule generation application using multiset decision tables
publisher	University of Akron / OhioLINK
publishDate	2005
url	http://rave.ohiolink.edu/etdc/view?acc_num=akron1140111266
work_keys_str_mv	AT gundavarapumadhavi rulegenarulegenerationapplicationusingmultisetdecisiontables
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RuleGen – A Rule Generation Application Using Multiset Decision Tables

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