This paper presents FlexlQ, a framework for feedback based query refinement. In FlexIQ, feedback is used to discover the query intent of the user and skyline operator is used to confine the search space of the proposed query refinement algorithms. The feedback consists of both unexpected information currently present in the query output and expected information that is missing from the query output. Once the feedback is given by the user, our framework refines the initial query by exploiting skyline operator to minimize the unexpected information as well as maximize the expected information in the refined query output. We validate our framework both theoretically and experimentally. In particular, we demonstrate the effectiveness of our framework by comparing its performance with decision tree based query refinement.
Lecture notes in computer science: Proceedings of the 31st International Conference on Conceptual Modeling (ER 2012), Florence, Italy, 15-18 October 2012 / Paolo Atzeni, David Cheung and Sudha Ram (eds.),
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