Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovering all most specific sentences
ACM Transactions on Database Systems (TODS)
Mining unexpected rules by pushing user dynamics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Probabilistic information retrieval approach for ranking of database query results
ACM Transactions on Database Systems (TODS)
\delta-Tolerance Closed Frequent Itemsets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Query Recommendations for Interactive Database Exploration
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Measure-driven keyword-query expansion
Proceedings of the VLDB Endowment
How to ConQueR why-not questions
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
FACeTOR: cost-driven exploration of faceted query results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
YmalDB: a result-driven recommendation system for databases
Proceedings of the 16th International Conference on Extending Database Technology
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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Typically, users interact with database systems by formulating queries. However, many times users do not have a clear understanding of their information needs or the exact content of the database, thus, their queries are of an exploratory nature. In this paper, we propose assisting users in database exploration by recommending to them additional items that are highly related with the items in the result of their original query. Such items are computed based on the most interesting sets of attribute values (or faSets) that appear in the result of the original user query. The interestingness of a faSet is defined based on its frequency both in the query result and in the database instance. Database frequency estimations rely on a novel approach that employs an e-tolerance closed rare faSets representation. We report evaluation results of the efficiency and effectiveness of our approach on both real and synthetic datasets.