Serving the Sky: Discovering and Selecting Semantic Web Services through Dynamic Skyline Queries
Serving the Sky: Discovering and Selecting Semantic Web Services through Dynamic Skyline Queries
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DOI：
10.1109/icsc.2008.65
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
Dimitrios Skoutas;Dimitris Sacharidis;A. Simitsis;T. Sellis
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文献类型：
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作者：
Dimitrios Skoutas;Dimitris Sacharidis;A. Simitsis;T. Sellis
语义Web服务描述通常是多参数构造。在给定期望的服务描述的情况下，发现语义相关的服务通常通过在请求的和提供的参数之间执行成对的、基于逻辑的匹配来解决。然而，很少或根本没有注意到组合这些部分结果来编译候选服务的最终列表。相反，这通常是以一种特别的方式完成的，这意味着关于用户偏好的先验假设。在本文中，我们的重点是在给定所请求的服务的描述的情况下识别最佳候选语义Web服务。我们将问题建模为天际线查询，也称为最大向量问题，并展示了如何有效地执行服务选择过程。我们考虑服务选择过程的不同方面，同时考虑请求者和提供者的观点。对真实数据和合成数据的实验评估表明了该方法的有效性和高效性。
Semantic Web service descriptions are typically multi-parameter constructs. Discovering semantically relevant services given a desirable service description is typically addressed by performing a pairwise, logic-based match between the requested and offered parameters. However, little or no attention is given to combining these partial results to compile the final list of candidate services. Instead, this is often done in an ad hoc manner, implying a priori assumptions regarding the user's preferences. In this paper, we focus on identifying the best candidate semantic Web services given the description of a requested service. We model the problem as a skyline query, also known as the maximum vector problem, and we show how the service selection process can be performed efficiently. We consider different aspects of the service selection process, addressing both the requesters' and the providers' points of view. Experimental evaluation on real and synthetic data shows the effectiveness and efficiency of the proposed approach.