VAGUE: a user interface to relational databases that permits vague queries
ACM Transactions on Information Systems (TOIS)
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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
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
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
Estimating recall and precision for vague queries in databases
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Characterization of Pareto dominance
Operations Research Letters
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SQL queries in relational data model implement the binary satisfaction of tuples. Tuples are generally filtered out from the result set if they miss the constraints expressed in the predicates of the given query. For naïve or inexperienced users posing precise queries in the first place is very difficult as they lack of knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for them. In connection with it, users are interested to have explanation of the missing answers. Even for unexpected tuples present in the result set advanced users may also want to know why a particular piece of information is present in the result set. This paper presents a simple model for generating explanations for both unexpected and missing answers. Further, we show how these explanations can be used to capture the user intent via feedback specifically for refining initial imprecise queries. The presented framework can also be thought as a natural extension for the existing SQL queries where support of explanation of expected and unexpected results are required to enhance the usability of relational database management systems. Finally, we summarize future research directions and challenges that need to be addressed in this endeavour.