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Web Service Compositions with Fuzzy Preferences: A Graded Dominance Relationship-Based Approach

机译：具有模糊首选项的Web服务组合：基于分级优势关系的方法

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摘要

Data-driven Web services build on service-oriented technologies to provide an interoperable method of interacting with data sources on top of the Web. Data Web services composition has emerged as a flexible solution to answer users' complex queries on the fly. However, as the number of Web services on the Web grows quickly, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query User preferences are a key factor that can be used to rank candidate services/compositions and retain only the best ones. In this article, we present a novel approach for computing the top-k data service compositions based on user preferences. In our approach, we model user preferences using fuzzy sets and incorporate them into the composition query. We use an efficient RDF query rewriting algorithm to determine the relevant services that may be used to answer the composition query. We match the (fuzzy) constraints of the relevant services to those of the query and determine their matching degrees using a set of matching methods. We then rank-order the candidate services based on a fuzzification of Pareto dominance and compute the top-k data service compositions. In addition, we introduce a new method for increasing the diversity of returned top-k compositions while maintaining as much as possible the compositions with the highest scores. Finally, we describe the architecture of our system and present a thorough experimental study of our proposed techniques and algorithms. The experimental study demonstrates the efficiency and the effectiveness of our techniques in different settings.
机译：数据驱动的Web服务基于面向服务的技术，以提供一种与Web上的数据源进行交互的可互操作的方法。数据Web服务组合已成为一种灵活的解决方案，可以随时响应用户的复杂查询。但是，随着Web上Web服务数量的快速增长，可能会使用大量使用不同（最有可能竞争）服务的候选组合来回答相同的查询。用户首选项是可用于进行排名的关键因素候选服务/组合，仅保留最佳组合。在本文中，我们提出了一种基于用户偏好来计算前k个数据服务组合的新颖方法。在我们的方法中，我们使用模糊集对用户偏好进行建模，并将其合并到组成查询中。我们使用高效的RDF查询重写算法来确定可用于回答组合查询的相关服务。我们将相关服务的（模糊）约束与查询的约束进行匹配，并使用一组匹配方法确定它们的匹配程度。然后，我们基于帕累托优势的模糊化对候选服务进行排序，并计算前k个数据服务组合。此外，我们引入了一种新方法，可增加返回的前k个组合的多样性，同时尽可能多地保留得分最高的组合。最后，我们描述了系统的体系结构，并对提出的技术和算法进行了全面的实验研究。实验研究证明了我们的技术在不同环境下的效率和有效性。

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