Reduction of Search Space in Restful Service Discovery

Abstract

Web Services has been enabled IT services and computing technology to perform business services more efficiently and effectively. REpresentational State Transfer (REST) is to be used for creating Web APIs/services. In the existing system, web service search engines for RESTful Web Services/Api’s provide Keyword, Tag and Semantic based search functions. One of the RESTful service discovery, referred as Test-oriented RESTful service discovery with Semantic Interface Compatibility (TASSIC) have been developed by the search of RESTful Service’s/Api’s. TASSIC approach will search the semantic characteristics of search and match interface terms in the service document. An inability to consider the classification and in finding the suitable Api’s or services are a key issue of the search space in Tassic. A new approach has proposed for reduction of the search space in restful service discovery to develop a k-Nearest Neighbor classification algorithm. it provide candidate services with ranking based on semantic similarity, and classifying of similar candidate services and service unit testing will be considered. This approach is meant for increasing search precision in the retrieval and quick search for classifying their RESTful services or Api according to user-defined criteria.