Name
Abstract | ||
|---|---|---|
Service composition is widely used to build complex value-added composite services to meet various coarse-grained requirements of customers. Discovering relevant services as the constituents of composite services is a crucial task, which needs to be frequently performed during the composition process. Due to the fact that the amount of services available on the Internet is increasing drastically, the efficiency of both service discovery and composition becomes a big challenge. To solve this challenge, we propose a Priori Knowledge Based Service Composition (PKBSC) approach to reduce the searching space of relevant service discovery so as to improve the efficiency of service composition. PKBSC utilizes an interoperable approach, including an ontology construction and merging method, to solve the problem of the cross-domain and heterogeneous services from different repositories. In addition, service pattern is adopted to describe priori knowledge from massive historical solutions, which is a recurrent valuable fragment composed of services frequently invoked together in service solutions. PKBSC also adopts the Formal Concept Analysis to extract the implicit relationship between service requests and service patterns. Compared with the approach of composing multiple services from scratch, PKBSC exhibits better performance since the search space is greatly reduced by the adoption of service patterns. Experiments demonstrate that the proposed approach significantly improves the efficiency of service composition by 22.44%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2879120 | IEEE ACCESS |
Keywords | Field | DocType |
Formal concept analysis, frequent pattern mining, service composition, service pattern | Ontology (information science),Task analysis,Interoperability,Computer science,Semantic Web,Service discovery,Web service,Formal concept analysis,The Internet,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruilin Liu | 1 | 159 | 17.17 |
| Xiaofei Xu | 2 | 408 | 70.26 |
| Zhong-Jie Wang | 3 | 356 | 64.60 |
| Quan Z. Sheng | 4 | 3520 | 301.77 |