Parallel Reasoning of Graph Functional Dependencies

Wenfei Fan, Xueli Liu, Yingjie Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper develops techniques for reasoning about graph functional dependencies (GFDs). We study the satisfiability problem, to decide whether a given set of GFDs has a model, and the implication problem, to decide whether a set of GFDs entails another GFD. While these fundamental problems are important in practice, they are coNP-complete and NP-complete, respectively. We establish a small model property for satisfiability, showing that if a set Σ of GFDs is satisfiable, then it has a model of a size bounded by the size |Σ| of Σ; similarly we prove a small model property for implication. Based on the properties, we develop algorithms for checking the satisfiability and implication of GFDs. Moreover, we provide parallel algorithms that guarantee to reduce running time when more processors are used, despite the intractability of the problems. We experimentally verify the efficiency and scalability of the algorithms.
Original languageEnglish
Title of host publication34th IEEE International Conference on Data Engineering 2018 (ICDE)
Place of PublicationParis, France
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages12
ISBN (Electronic)978-1-5386-5520-7
ISBN (Print)978-1-5386-5521-4
Publication statusPublished - 25 Oct 2018
Event34th IEEE International Conference on Data Engineering - Paris, France
Duration: 16 Apr 201819 Apr 2018

Publication series

ISSN (Print)1063-6382
ISSN (Electronic)2375-026X


Conference34th IEEE International Conference on Data Engineering
Abbreviated titleICDE 2018
Internet address


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