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基于图的语义 Web 服务组合在医疗保健数据集成中的应用。

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

机构信息

Semantic Mining and Information Integration Laboratory (SMIIL), Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand.

Graduate School, College of Asian Scholars, Khon Kaen 40000, Thailand.

出版信息

J Healthc Eng. 2017;2017:4271273. doi: 10.1155/2017/4271273. Epub 2017 Aug 20.

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.

摘要

在不同来源提供的众多异构 Web 服务中,自动 Web 服务组合是构建允许调用多个现有原子服务的复杂业务流程的最便捷方法。当前的功能 Web 服务组合解决方案在 Web 服务参数内缺乏对语义匹配的自主查询,而这在大规模相关服务的组合中是必要的。在本文中,我们提出了一种基于图的语义 Web 服务组合系统,它由两个子系统组成:管理时间和运行时间。管理时间子系统负责准备依赖图,根据提出的语义匹配规则自动生成相关服务的依赖图。运行时子系统负责使用基于图的搜索算法发现用户查询的潜在 Web 服务和非冗余 Web 服务组合。该方法应用于不同医疗机构的医疗保健数据集成,并根据两个方面进行评估:执行时间测量和正确性测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/002a/5591970/ea46eeec12e4/JHE2017-4271273.001.jpg

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