Abstract Views :168 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Bharathidasan University, IN
Source
ICTACT Journal on Soft Computing, Vol 2, No 4 (2012), Pagination: 377-383
Abstract
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.
Keywords
Optimization Infrastructure, Service Cache, Service Cluster, Service Index, Semantic Service Discovery.
