Facilitating web service selection through efficient clustering approaches

Abstract

Web services are garnering widespread acceptance in modern applications and have revolutionized the way industry and public sectors operate The utilization of web services over World Wide Web WWW is growing quickly as the necessity for application-to-application communication and interoperability is rising Web services deliver a standard means of communication among diverse software applications working over numerous platforms and/or frameworks Web services offer many technological and business aids They allow applications to communicate with any other application and effectively interchange data without the need to know the underlying implementation or data formats This service can be utilized by several clients to accomplish various business objectives It contains many advantages but still the perfect web service selection is a highly challenging task because the existing web service selection that depends on Universal Description Discovery and Integration UDDI registries is not very much constructive To overcome these kinds of difficulties in web service selection the current research has been carried out In the initial scheme web service clustering has been carried out through a two phase clustering approach The two phase clustering approach has been implemented through Adaptive Resonance Theory ART with Swarm based algorithm birds flocking or boids algorithm This approach provides two levels of input quantification to the clustering algorithm The first part of the input feature includes functional requirements and the second part involves non-functional requirements newline