Utilizing linked open data for web service selection and composition to support e-commerce transactions
Web Services (WS) have emerged during the past decades as a means for loosely coupled distributed systems to interact and communicate. Nevertheless, the abundance of services that can be retrieved online, often providing similar functionalities, can raise questions regarding the selection of the optimal service to be included in a value added composition. We propose a framework for the selection and composition of WS utilizing Linked open Data (LoD). The proposed method is based on RDF triples describing the functional and non-functional characteristics of WS. We aim at the optimal composition of services as a result of specific SPARQL queries and personalized weights for QoS criteria. Finally we utilize an approach based on the particle swarm optimization (PSO) method for the ranking of returned services.
In the overall ownership cost of enterprise system, the maintenance cost consists of a major percentage. During the lifetime of an enterprise system, process customization is the most frequent maintenance efforts. However, current processing method has limited scalability and efficiency. In this case study, we explained how a scalable and efficient customization processing method was implemented. This method used the carbon emission trading mechanism to facilitate the cost benefit analysis of customization request. It also used distributed processing principle to improve the overall processing efficiency. Feedback from a pilot implementation in a large manufacturer included.