A Pattern-Recognition-Based Algorithm and Case Study for Clustering and Selecting Business Services

Positioned as the backbone of service asset management console, a service registry has to enable real-time and offline service selection in an effective manner. This paper presents an analytic algorithm that is used to guide the architectural design of service exploration in a service registry. Service assets are proposed to be framed into a well-established categorical structure based on pattern recognition algorithm. This design aims to provide systematic methodology and enablement architecture for analyzing, clustering, and adapting heterogeneous services for dynamic application integration. The exploitation of pattern recognition algorithm maps a large amount of services into a manageable feature space, which consists of attributes that are related to static description and dynamic features, such as historical QoS and service-level agreement. The proposed architecture and associated service exploration methodology have been integrated into an industry strength service-oriented architecture solution design platform. We also present a case study using the developed platform to illustrate the proposed algorithm for business service clustering and selection.