An approach for web service selection and dynamic composition based on linked open data

Vesyropoulos, Nikolaos; Georgiadis, Christos; Pimenidis, Elias

Abstract

The wide adoption of the Service Oriented Architecture (SOA) paradigm has provided a means for heterogeneous systems to seamlessly interact and exchange data. Thus, enterprises and end-users have widely utilized Web Services (WS), either as stand-alone applications or as part of more complex service compositions, in order to fulfill their business needs. But, while WS offer a plethora of benefits, a significant challenge rises due to the abundance of available services that can be retrieved online. In this work, we propose a framework for the selection and dynamic composition of WS, by utilizing Linked open Data (LoD). In addition, we propose a hybrid algorithm that uses as input the user's personalized weights for non-functional characteristics and the results produced by appropriate SPARQL queries that are filtered results using a top-k approach. It then handles the ranking of alternatives based on their population. Finally, using two case studies and a dataset that describes real-world WS, we argue on the feasibility and performance of the proposed method.

Citation

Vesyropoulos, N., Georgiadis, C., & Pimenidis, E. (2018). An approach for web service selection and dynamic composition based on linked open data. In N. T. Nguyen, & R. Kowalczyk (Eds.), Transactions on Computational Collective Intelligence XXX (54-70). Springer. https://doi.org/10.1007/978-3-319-99810-7_3

• Acceptance Date: Oct 20, 2017
• Publication Date: Sep 24, 2018
• Deposit Date: Oct 23, 2017
• Publicly Available Date: Sep 25, 2020
• Journal: LNCS Transactions on Computational Collective Intelligence
• Print ISSN: 2190-9288
• Peer Reviewed: Not Peer Reviewed
• Pages: 54-70
• Series Title: Lecture Notes in Computer Science
• Book Title: Transactions on Computational Collective Intelligence XXX
• ISBN: 9783319998091
• DOI: https://doi.org/10.1007/978-3-319-99810-7_3
• Keywords: Web Services, Web Service Composition, Linked open Data, RDF, Particle Swarm Optimization
• Public URL: https://uwe-repository.worktribe.com/output/880643
• Publisher URL: https://doi.org/10.1007/978-3-319-99810-7_3
• Additional Information: Additional Information : The final publication is available at Springer via https://doi.org/10.1007/978-3-319-99810-7_3