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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 4271273, 19 pages
https://doi.org/10.1155/2017/4271273
Research Article

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

1Semantic Mining and Information Integration Laboratory (SMIIL), Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand
2Graduate School, College of Asian Scholars, Khon Kaen 40000, Thailand

Correspondence should be addressed to Ngamnij Arch-int; ht.ca.ukk@jinmagn, Somjit Arch-int; ht.ca.ukk@tijmos, Suphachoke Sonsilphong; ht.ca.sac@ekohcahpus, and Paweena Wanchai; ht.ca.ukk@aneewapw

Received 11 April 2017; Revised 4 June 2017; Accepted 18 June 2017; Published 20 August 2017

Academic Editor: Jiang Bian

Copyright © 2017 Ngamnij Arch-int et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user’s query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.