VAGUE: a user interface to relational databases that permits vague queries
ACM Transactions on Information Systems (TOIS)
Machine Learning
Modern Information Retrieval
Proceedings of the 17th International Conference on Data Engineering
Answering imprecise database queries: a novel approach
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Answering imprecise queries over web databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Report on the DB/IR panel at SIGMOD 2005
ACM SIGMOD Record
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Making database systems usable
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Preference SQL: design, implementation, experiences
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On the provenance of non-answers to queries over extracted data
Proceedings of the VLDB Endowment
Efficient skyline querying with variable user preferences on nominal attributes
Proceedings of the VLDB Endowment
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The perm provenance management system in action
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Provenance in Databases: Why, How, and Where
Foundations and Trends in Databases
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A Model for Contextual Cooperative Query Answering in E-Commerce Applications
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Feedback-driven result ranking and query refinement for exploring semi-structured data collections
Proceedings of the 13th International Conference on Extending Database Technology
Feedback-based annotation, selection and refinement of schema mappings for dataspaces
Proceedings of the 13th International Conference on Extending Database Technology
How to ConQueR why-not questions
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Optimizing visual search with implicit user feedback in interactive video retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Explaining missing answers to SPJUA queries
Proceedings of the VLDB Endowment
Automatic rule refinement for information extraction
Proceedings of the VLDB Endowment
SIRE: a social image retrieval engine
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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
Characterization of Pareto dominance
Operations Research Letters
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SQL queries in the existing relational data model implement the binary satisfaction of tuples. That is, a data tuple is filtered out from the result set if it does not satisfy the constraints expressed in the predicates of the user submitted query. Posing appropriate queries for ordinary users is very difficult in the first place if they lack knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for many users. In connection with this, this paper presents a framework for capturing user intent through feedback for refining the initial imprecise queries that can fulfill the users' information needs. The feedback in our framework consists of both unexpected tuples currently present in the query output and expected tuples that are missing from the query output. We show that our framework does not require users to provide the complete set of feedback tuples because only a subset of this feedback can suffice. We provide the point domination theory to complement the other members of feedback. We also provide algorithms to handle both soft and hard requirements for the refinement of initial imprecise queries. Experimental results suggest that our approach is promising compared to the decision tree based query refinement approach.