A Novel Event Rule Derivation for Processing Uncertain Data Events Using Genetic Network Programming

International Journal of Computer & Organization Trends  (IJCOT)          
© 2014 by IJCOT Journal
Volume - 4 Issue - 3
Year of Publication : 2014
Authors :  M. Sivasankari
DOI :  10.14445/22492593/IJCOT-V9P302


M. Sivasankari. "A Novel Event Rule Derivation for Processing Uncertain Data Events Using Genetic Network Programming", International Journal of Computer & organization Trends (IJCOT), V4(3):72-76 May - June 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.


Continuously growing size and number of databases in a variety of domains has boosted development of numerous data mining methods during the last decade. There is an increasing requirement to discover associations and relations among large and uncertain databases, which may be tackled by association rule mining. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. Current work proposes a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. To solve this problem, the proposed system uses Genetic Network Programming (GNP) for event rule derivation. A method for association rule mining from large, heterogeneous and uncertain databases is proposed using an evolutionary method named Genetic Network Programming (GNP). Some other association rule mining methods cannot handle uncertain data directly, they are inapplicable or computational inefficient under such a model. GNP utilizes direct graph structure and is able to extract rules without generating frequent item sets to improve mining efficiency..


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Complex event processing, rule-based reasoning with uncertain information, Genetic Network Programming