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Discovering graph functional dependencies

  • University of Edinburgh
  • Beihang University
  • Harbin Institute of Technology

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

Abstract

This paper studies discovery of GFDs, a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms for discovering GFDs that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.

Original languageEnglish
Title of host publicationSIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data
EditorsGautam Das, Christopher Jermaine, Ahmed Eldawy, Philip Bernstein
PublisherAssociation for Computing Machinery
Pages427-439
Number of pages13
ISBN (Electronic)9781450317436
DOIs
StatePublished - 27 May 2018
Event44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 - Houston, United States
Duration: 10 Jun 201815 Jun 2018

Publication series

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

Conference

Conference44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
Country/TerritoryUnited States
CityHouston
Period10/06/1815/06/18

Keywords

  • Fixed-parameter tractability
  • GFD discovery
  • Parallel scalable

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