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YmalDB: exploring relational databases via result-driven recommendations

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Abstract

The typical user interaction with a database system is through queries. However, many times users do not have a clear understanding of their information needs or the exact content of the database. In this paper, we propose assisting users in database exploration by recommending to them additional items, called Ymal (“You May Also Like”) results, that, although not part of the result of their original query, appear to be highly related to it. Such items are computed based on the most interesting sets of attribute values, called faSets, that appear in the result of the original query. The interestingness of a faSet is defined based on its frequency in the query result and in the database. Database frequency estimations rely on a novel approach of maintaining a set of representative rare faSets. We have implemented our approach and report results regarding both its performance and its usefulness.

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Acknowledgments

The research of the first author has been co-financed by the European Union (ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the NSRF - Research Funding Program: Heracleitus II. The research of the second author has been co-financed by the European Union (ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the NSRF - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund EICOS project.

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  1. Computer Science Department, University of Ioannina, Ioannina, Greece

    Marina Drosou & Evaggelia Pitoura

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  1. Marina Drosou
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  2. Evaggelia Pitoura
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Correspondence to Marina Drosou.

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Drosou, M., Pitoura, E. YmalDB: exploring relational databases via result-driven recommendations. The VLDB Journal 22, 849–874 (2013). https://doi.org/10.1007/s00778-013-0311-4

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