Pattern Recognition Based Adaptive Categorization Technique and Solution for Services Selection

The current design for the service registry architecture lacks a well-organized categorical structure and service- aware exploration method to enable effective real-time and offline services selection. To address this issue, this paper proposes an architectural framework and enabling technology for a business services analyzer that supports analyzing, clustering and adapting heterogeneous services for dynamic application integration. The proposed systematic services exploration methodology includes services categorization, services clustering and services exposure. By applying pattern recognition algorithm, we build a manageable feature space that is able to select and expose a service to serve the request from a repository with "large" amount of available services. To illustrate our design, we also provide a research prototype called Services Litmus Test (SLT) toolkit, which provides a flexible software platform for executing systematic services exploration procedures. The GUI based human assisted tune-up interface makes it very convenient for the services system designers to customize their design according to the adaptive system requirements.