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Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into service-oriented architecture (SOA), and the adoption of radio frequency identification (RFID), event processing can fit well in enterprise information systems in terms of facilitation of event aggregation into high level actionable information, and event response to improve the responsiveness. To make it more applicable, the architecture of event processing in enterprise information systems is proposed; event meta model and context serve as the solid basis for event processing; the rules, operators and keys of complex event processing are defined. Especially, workflow model is firstly used to extract complex event pattern. We have implemented the event processing mechanism in enterprise information systems based on RFID, including the architecture, data structures, optimization strategies and algorithm. The performance evaluations show that the method is effective in terms of scalability and the capability of event processing. Complex event processing can improve operational performance and discover more actionable information, which is justified by application. Finally, lessons learned from the application are presented.

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A service ecosystem consists of services and their compositions (i.e., mashups) and evolves as a complex network system. It is driven by continuously emerged new services and the mashups of old services and new ones. Complex network analysis can be a powerful tool to study the static structure as well as the evolution of a service ecosystem. This paper presents a methodology to study such a system and an empirical study of ProgrammableWeb. To the best of our knowledge, ProgrammableWeb is the largest and most active Web APIs and mashups collection and consists of 4337 services and 6092 service compositions by Nov-2011. We conduct a comprehensive network analysis to quantitatively characterize the static structure and dynamic evolution of the ecosystem. The findings of this paper not only can help understand the current usage pattern and the evolution trace of the ecosystem, but also are applicable to other Web service systems.

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