vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff09c80b000000d48d010001000300 The process of semantic service discovery using an ontology reasoner such as Pellet is time consuming. This restricts the usage of web services in real time applications having dynamic composition requirements. As performance of semantic service discovery is crucial in service composition, it should be optimized. Various optimization methods are being proposed to improve the performance of semantic discovery. In this work, we investigate the existing optimization methods and broadly classify optimization mechanisms into two categories, namely optimization by efficient reasoning and optimization by efficient matching. Optimization by efficient matching is further classified into subcategories such as optimization by clustering, optimization by inverted indexing, optimization by caching, optimization by hybrid methods, optimization by efficient data structures and optimization by efficient matching algorithms. With a detailed study of different methods, an integrated optimization infrastructure along with matching method has been proposed to improve the performance of semantic matching component. To achieve better optimization the proposed method integrates the effects of caching, clustering and indexing. Theoretical aspects of performance evaluation of the proposed method are discussed.
Chellammal Surianarayanan , Gopinath Ganapathy
Bharathidasan University, India
Optimization Infrastructure, Service Cache, Service Cluster, Service Index, Semantic Service Discovery
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 2 , Issue: 4 , Pages: 377-383 )
Date of Publication :
July 2012
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190
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