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A service composition evolution method that combines deep clustering and a service requirement context model.
- Source :
-
Expert Systems with Applications . Aug2023, Vol. 224, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Service composition can quickly build new value-added composite services by combining existing Web services. However, with the complex and changeable Internet environment, composite services need to effectively understand and manage requirements in a flexible and adaptive manner, and also be able to quickly and proactively provide high-quality services through a series of dynamic evolution models. In response to these challenges, in this study, we propose a service composition evolution method that can adaptively select the appropriate service to improve user satisfaction and service quality. First, we introduce a deep clustering method based on the topic model and auto-encoder, which considers the function description documents and parameter information of the services to reduce the search space of the candidate services. Furthermore, we describe a requirement-oriented context-sensitive task model to integrate functional and non-functional user requirements by connecting the context of Web subtasks. Then, we present a dynamic service matching method with QoS threshold judgement to filter and rank the services. We applied the proposed evolution method to the datasets of real-world Web services and measured the performance using standard measurement metrics. The prototype implementation and results of simulation experiments verified the effectiveness of each part of our method. • A novel service composition evolution method is proposed. • A deep clustering method is presented to implement the service clustering process. • RCT model and various evolutionary types are used to satisfy user requirements. • Simulation experiments on prototype system show the effectiveness. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 224
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 163514209
- Full Text :
- https://doi.org/10.1016/j.eswa.2023.119920