2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)

Paris, France

Apr 16, 2018 to Apr 19, 2018

ISSN: 2375-026X

ISBN: 978-1-5386-5520-7

pp: 593-604

ABSTRACT

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 ? 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.

INDEX TERMS

computability, computational complexity, graph theory, parallel algorithms

CITATION

W. Fan, X. Liu and Y. Cao, "Parallel Reasoning of Graph Functional Dependencies,"

*2018 IEEE 34th International Conference on Data Engineering (ICDE)*, Paris, France, 2018, pp. 593-604.

doi:10.1109/ICDE.2018.00060

CITATIONS