Optimizing queries to remote resources
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One key property of the Semantic Web is its support for interoperability. Recent research in this area focuses on the integration of multiple data sources to facilitate tasks such as ontology learning, user query expansion and context recognition. The growing popularity of such machups and the rising number of Web APIs supporting links between heterogeneous data providers asks for intelligent methods to spare remote resources and minimize delays imposed by queries to external data sources. This paper suggests a cost and utility model for optimizing such queries by leveraging optimal stopping theory from business economics: applications are modeled as decision makers that look for optimal answer sets. Queries to remote resources cause additional cost but retrieve valuable information which improves the estimation of the answer set’s utility. Optimal stopping optimizes the trade-off between query cost and answer utility yielding optimal query strategies for remote resources. These strategies are compared to conventional approaches in an extensive evaluation based on real world response times taken from seven popular Web services.
KeywordsInformation integration Adaptive decision-making Optimal stopping Opportunity cost model Semantic Web Heterogeneous data sources
The project results have been developed in the RAVEN (Relation Analysis and Visualization) project funded by the Austrian Ministry of Transport, Innovation and Technology and the Austrian Research Promotion Agency. The author would like to thank Wolfgang Janko for his valuable suggestions during the preparation of this article.
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