JAMEJAM: A Framework for Automating the Service Discovery Process

The service discovery problem is not trivial, as it requires solutions for many complex sub-problems such as service semantic description, service identification, service composition, service selection, service evaluation, service adaptation and presentation. Currently, companies manually construct their discovery processes in an ad-hoc tightly-coupled manner using different platform-services that separately handle the identification, composition, selection, evaluation, adaptation and presentation sub-problems. However, when users’ requirements change, the already constructed discovery process needs to be manually reconstructed and reevaluated again. This creates a need for an automated approach that allows different users to dynamically construct their discovery processes on the fly. Therefore, we propose JAMEJAM, a framework for service discovery automation. It enables users to create their customizable discovery processes on demand as an executable BPEL process that describes the required matching aspects, matching schemes and matching policies. JAMEJAM realizes such process by dynamically searching for the suitable platform-services in a context-sensitive manner using different types of knowledge (e.g., aspects, services, and matching schemes knowledge), captured via different software ontologies. Experimental results show that JAMEJAM increases the accuracy and the adaptability of the service discovery process.