Representative Query Results by Voting

Rachel Behar, Sara Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Traditional query answering returns all answers T to a given query. When T is large, the user may be interested in viewing only a smaller subset S of T. Previous work has focused on finding subsets S that are diverse, i.e., such that all items s,s' in S are very different one from another. This paper focuses on a complementary problem, namely finding subsets that are highly representative of the entire set of query results. Intuitively, a representative subset S is similar, in values and proportionality, to the entire set T. Finding such a representative set is challenging, both conceptually, and in practice. This paper proposes a novel method of choosing a representative subset, called SimSTV, which draws inspiration from the field of voting theory. An efficient algorithm is presented, which overcomes and leverages the many differences between choosing answers in a database, and voting in a real-life election. We also provide extensions to our algorithm, e.g., to accommodate affirmative action. Experimental results show the effectiveness of our algorithm.

Original languageAmerican English
Title of host publicationSIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1741-1754
Number of pages14
ISBN (Electronic)9781450392495
DOIs
StatePublished - 10 Jun 2022
Event2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022 - Virtual, Online, United States
Duration: 12 Jun 202217 Jun 2022

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022
Country/TerritoryUnited States
CityVirtual, Online
Period12/06/2217/06/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • diversity
  • query answering
  • representatives

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