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Discovering Graph Functional Dependencies

CategoryPublications Publications 298

Authors: Wenfei Fan, Chunming Hu , Xueli Liu, and Ping Lu

Published in: ACM Transactions on Database Systems (TODS 2020)

Date of Publication: Sept 1, 2020

Abstract

This article studies discovery of Graph Functional Dependencies (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 in general. 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 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.

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Keywords2020Data qualityTODSWenfei Fan Previous: Next: