Modelling and exploring historical records to facilitate service composition
Along with a continuously growing number of Web services, how to locate appropriate Web services to complete the task of service composition is becoming more critical. Differing from most recent studies which mainly focus on functional and non-functional properties, we mine nuggets from the Historical Service-composition Dataset (HSD), which carries related users' past experiences. In this paper, a graph mining based recommendation approach is presented to facilitate the process of service composition. In particular, we first extend the graph mining approach gSpan to recognise Frequently Used Web Services (FUWS) with their connecting structures from HSD. Then, according to the records in HSD, which share the same FUWSs with user's partially composed service, a bunch of Web services with higher probability is recommended automatically as candidates. Finally, the skyline approach is adopted for optimal composite service selection with consideration of overall quality of services (QoS). Furthermore, experiments based on 1,530 real Web services demonstrate the effectiveness and efficiency of our approach.
web service graph mining recommendation skyline qos