Mining Bilateral Patterns as Priori Knowledge for Efficient Service Composition

It is still a great challenge to make composite services that fulfill massive requirements from large community of customers. Service network (SN) approach has been put forward to deal with this issue in a cost-effective and agile way, i.e., a large number of services are connected as a customizable SN in terms of underlying semantics correlations, and when a new requirement arrives, the SN is customized to generate a composite service. It has been observed that there are similarities among requirements, and so are there among composite solutions of these requirements. In this paper, we identify bilateral patterns (i.e., requirement patterns and service patterns) from historical service composition records, and then establish the probabilistic mappings between them, consequently, a bilateral pattern based service network customization algorithm named BPSC is put forward to take full advantage of such priori knowledge to speed up the customization process. From experiments we find that, compared with traditional SN customization approaches such as WSPR, our BPSC can significantly improve the efficiency in most instances.