The similarity-aware relational database set operators

Wadha J. Al Marri, Qutaibah Malluhi, Mourad Ouzzani, Mingjie Tang, Walid G. Aref*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Identifying similarities in large datasets is an essential operation in several applications such as bioinformatics, pattern recognition, and data integration. To make a relational database management system similarity-aware, the core relational operators have to be extended. While similarity-awareness has been introduced in database engines for relational operators such as joins and group-by, little has been achieved for relational set operators, namely Intersection, Difference, and Union. In this paper, we propose to extend the semantics of relational set operators to take into account the similarity of values. We develop efficient query processing algorithms for evaluating them, and implement these operators inside an open-source database system, namely PostgreSQL. By extending several queries from the TPC-H benchmark to include predicates that involve similarity-based set operators, we perform extensive experiments that demonstrate up to three orders of magnitude speedup in performance over equivalent queries that only employ regular operators.

Original languageEnglish
Pages (from-to)79-93
Number of pages15
JournalInformation Systems
Volume59
DOIs
Publication statusPublished - 1 Jul 2016

Keywords

  • Relational databases
  • Set operators
  • Similarity query processing

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