Machine Learning
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the provenance of non-answers to queries over extracted data
Proceedings of the VLDB Endowment
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Integrating similarity retrieval and skyline exploration via relevance feedback
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
How to ConQueR why-not questions
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Explaining missing answers to SPJUA queries
Proceedings of the VLDB Endowment
Automatic rule refinement for information extraction
Proceedings of the VLDB Endowment
RAF: an activation framework for refining similarity queries using learning techniques
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Top-k combinatorial skyline queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Characterization of Pareto dominance
Operations Research Letters
Answering Why-not Questions on Top-k Queries
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
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This paper presents FlexIQ, 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.