Indexing method for effective web service retrieval. (1st January 2014)

• Record Type: Journal Article
• Authors: Czyszczoń, Adam; Zgrzywa, Aleksander
• Is Part Of: International journal of intelligent information and database systems. Volume 8:Number 3(2014)
• Page Start: 189
• Page End: 208
• Publication Date: 2014-01-01
• Subjects: indexing method -- web service retrieval -- effective retrieval -- information retrieval -- intelligent information systems -- intelligent database systems; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• DOI: 10.1504/IJIIDS.2014.066638
• Languages: English
• ISSNs: 1751-5858

Abstract

This paper addresses the problem of web service retrieval by presenting a new approach to indexing of both SOAP and RESTful web services. The approach uses index structure called parametric index that allows users to retrieve ranked results in accordance with specific parameters. The parameters refer to service's integral components and are covered in presented formal definition of a web service. Secondly, the services are modelled in vector space that allows the evaluation of their mutual relevance and enables obtaining ranked search results. In order to reduce the index size and to decrease the search time, presented approach uses the method of conceptual indexing which groups relevant service components into concepts. For further search performance improvement, this paper also introduces the concept of merged weight vector. The research is supported by novel indexing algorithm and implementation of proposed approach. Experimental results confirm that the presented approach significantly reduces the index size. It also improves search performance in contrast to standard indexing algorithm and compared to k-means and self-organising maps clustering algorithms.