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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-ß 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-ß data service compositions. In addition, we introduce a new method for increasing the diversity of returned top-ß 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. [ABSTRACT FROM AUTHOR]

XML-enabled publish-subscribe (pub-sub) systems have emerged as an increasingly important tool for e-commerce and Internet applications. In a typical pub-sub system, subscribed users specify their interests in a profile expressed in the XPath language. Each new data content is then matched against the user profiles so that the content is delivered only to the interested subscribers. As the number of subscribed users and their profiles can grow very large, the scalability of the service is critical to the success of pub-sub systems. In this article,we propose a novel scalable filtering system called iFiST that transforms user profiles of a twig pattern expressed in XPath into sequences using the Prüfer's method. Consequently, instead of breaking a twig pattern into multiple linear paths and matching them separately, iFiST performs holistic matching of twig patterns with each incoming document in a bottom-up fashion. iFiST organizes the sequences into a dynamic hash-based index for efficient filtering, and exploits the commonality among user profiles to enable shared rocessing during the filtering phase. We demonstrate that the holistic matching approach reduces filtering cost and memory consumption, thereby improving the scalability of iFiST. [ABSTRACT FROM AUTHOR]