Configuration and Adaptation of Semantic Web Processes

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As Web services and service oriented architectures become pervasive in the business and scientific environments, there has been a growing focus on representing business and scientific processes using Web service based processes or Web processes. While workflow and other automation technologies have existed for a couple of decades, tools and frameworks in this space do not provide adequate support for the dynamism and adaptability required to represent and execute real world processes. With technological advances (e.g., RFID) that help in generating real time data, the next generation of Web process frameworks must evolve to provide capabilities for handling and reacting to such events. In addition, the large scale standardization of all aspects of businesses has set the stage for businesses to configure their processes on the fly with new or pre-existing business partners. This thesis is one of the first attempts to create a comprehensive framework for dynamic configuration and adaptation of Web processes. While we have evaluated this framework in the context of a supply chain, we believe that this framework can also be applied to other business and scientific processes. Our work is based on using a semantic framework that uses ontologies and semantic descriptions of Web services as an enabler of the two capabilities. The semantic descriptions of Web services are based on our recent W3C member submission WSDL-S. Much work has been done in operations research for business process optimization. However, there is a lot of domain knowledge that is used in conjunction with operations research techniques by experts for decision making. We explore adding greater automation to this decision making by capturing this domain knowledge in ontologies and using it in conjunction with Integer Linear Programming for dynamic process configuration. The other problem we address is that of process adaptation. While other approaches exist for process adaptation, none of them have considered uncertainly about when the event may occur. We present adaptation as a stochastic decision making problem and present an approach that uses Markov Decision Processes. Both configuration and adaptation have been evaluated comprehensively and our results clearly demonstrate their benefits.