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Aggregate keyword search on large relational databases

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Abstract

Keyword search has been recently extended to relational databases to retrieve information from text-rich attributes. However, all the existing methods focus on finding individual tuples matching a set of query keywords from one table or the join of multiple tables. In this paper, we motivate a novel problem of aggregate keyword search: finding minimal group-bys covering a set of query keywords well, which is useful in many applications. We develop two interesting approaches to tackle the problem. We further extend our methods to allow partial matches and matches using a keyword ontology. An extensive empirical evaluation using both real data sets and synthetic data sets is reported to verify the effectiveness of aggregate keyword search and the efficiency of our methods.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Bin Zhou & Jian Pei

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  1. Bin Zhou
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  2. Jian Pei
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Correspondence to Bin Zhou.

Additional information

A preliminary version of this paper appears as [42]. The authors are grateful to the anonymous reviewers and the associate editor for their constructive comments that help to improve the quality of the paper. This research is supported in part by an NSERC Discovery Grant, an NSERC Discovery Accelerator Supplement Grant, and a British Columbia Natural Resources and Applied Sciences Endowment Fund. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.

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Zhou, B., Pei, J. Aggregate keyword search on large relational databases. Knowl Inf Syst 30, 283–318 (2012). https://doi.org/10.1007/s10115-011-0379-3

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