The Community for Technology Leaders
2016 IEEE International Conference on Web Services (ICWS) (2016)
San Francisco, CA, USA
June 27, 2016 to July 2, 2016
ISBN: 978-1-5090-2676-0
pp: 324-331
In the era of Big Data, data analysis gives strong competition power to enterprises. As services for Big Data Analysis (BDA) become prevalent, analysis services with intelligence and autonomy using automatic service composition show very bright prospects in the BDA market. Service composition consists of four stages: workflow generation, discovery, selection, and execution. In this paper, we propose a novel service discovery approach that considers two key concerns in the discovery domain towards better quality as well as effective service composition. BDA services are fine grained according to the domain and functional behaviors. The services need a domain context-aware and precision-guided discovery approach. Therefore, we propose domain ontology-based service discovery. It is mainly focused on the BDA domain for precise service discovery considering all behavioral signatures between queries and services. As for the second concern, components in composed services depend greatly on each other in situations such as workflow for data analysis. We show that linking services together considering sociability or user preference gives better discovery performance. We propose a Linked Social Service Network (LSSN) with multiple feature attribute-based service discovery for BDA. Our approach combines two advantages, the precision and sociability of Web services. The experimental results show that both of these methods perform well based on their perspectives, better than previous approaches.
Ontologies, Big data, Data models, Analytical models, Web services, Semantics, High-temperature superconductors

T. A. Siriweera, I. Paik, J. Zhang and B. T. Kumara, "Big Data Analytic Service Discovery Using Social Service Network with Domain Ontology and Workflow Awareness," 2016 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA, 2016, pp. 324-331.
86 ms
(Ver 3.3 (11022016))