AI powered gpu enabled semantic web service composition

Abstract

Web services are an essential part of the world wide web which simplifies automation in each business domain. The popularity of web services are increasing day by day with a sudden shift of people into the digital lifestyle. Web Services paves the way into automation of every functionality around us. As the scope for automation increases, the total number of web services developed also increases in an exponential fashion. Every company strives to become a global leader of automation which leads to increased competitiveness across them which in turn leads to an increase in functionally similar web services across the web. The presence of numerous amount of web services increases the challenge involved in the retrieval of web services for a user query. This research work aims in formulating a framework that makes the retrieval of web services efficient. Web services are granular in nature and hence a complex user query in most cases cannot be satisfied by the retrieval of a single atomic service. Hence, it becomes necessary to identify a collection of services which when executed in a particular order satisfy the user query. To facilitate this process of service composition, the framework is divided into three sequential phases namely Web Service Discovery, Web Service Selection, and Plan Generation. This research work aims in contributing towards each phase to ease out the process service composition. Web Service Discovery is the initial step for composing the services where every service has to be functionally detected accurately, such that it can solve an aspect of the user query. The researchers have employed various techniques like Match Making Algorithms, Optimization Techniques newline