MDT: A Multi Description Topic Based Clustering Approach for Composite Service Discovery
With the rapid development of Service Oriented Architecture that enables rapid composition and integration of large scale distributed systems, the number of web accessible services and their compositions is increasing rapidly, which makes it difficult to efficiently discover desired composite services from a sea of candidates. Recently, topic based clustering techniques have emerged as a promising way to improve the efficiency and accuracy of service discovery. However, it is a challenge to extract topics from short description texts of composite services, for short texts do not provide sufficient word occurrences. In view of this problem, a multi description topic based clustering approach for composite service discovery is proposed. In this approach, descriptions of the component services of each composite service are combined for topic extraction and modeling. Then, composite services are clustered based on their topic similarities. Finally, composite services are discovered from the cluster that is most topic similar to the service queries. The effectiveness of the proposed approach was verified by some experiments on the real world data at the end of this paper.
2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)