research-article

Making It Tractable to Catch Duplicates and Conflicts in Graphs

Authors:

Chao TianAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 86, Pages 1 - 28

https://doi.org/10.1145/3588940

Published: 30 May 2023 Publication History

Abstract

This paper proposes an approach for entity resolution (ER) and conflict resolution (CR) in large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support the primitives of relational data cleaning rules, and may embed machine learning classifiers as predicates. As opposed to previous graph rules, GCRs are defined with a dual graph pattern to accommodate irregular structures of schemaless graphs, and adopt patterns of a star form to reduce the complexity. We show that the satisfiability, implication and validation problems are all in polynomial time (PTIME) for GCRs, as opposed to the intractability of these classical problems for previous graph dependencies. We develop a parallel algorithm to discover GCRs by combining the generations of patterns and predicates, and a parallel PTIME algorithm for "deep" ER and CR by recursively applying the mined GCRs. We show that these algorithms guarantee to reduce runtime when more processors are used. Using real-life and synthetic graphs, we experimentally verify that rule discovery and error detection with GCRs are substantially faster than with previous graph dependencies, with improved accuracy.

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Cited By

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https://dl.acm.org/doi/10.14778/3685800.3685858
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Index Terms

Making It Tractable to Catch Duplicates and Conflicts in Graphs
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Incomplete, inconsistent, and uncertain databases

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Proceedings of the ACM on Management of Data Volume 1, Issue 1

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May 2023

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DOI:10.1145/3603164

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Cited By

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https://dl.acm.org/doi/10.14778/3685800.3685858
Fan WLiu MLiu STian C(2024)Capturing More Associations by Referencing External GraphsProceedings of the VLDB Endowment10.14778/3648160.364816217:6(1173-1186)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648162
Yan MWang YPang KXie MLi JBaeza-Yates RBonchi F(2024)Efficient Mixture of Experts based on Large Language Models for Low-Resource Data PreprocessingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671873(3690-3701)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671873
Bian LYang WXu JTan ZSerra ESpezzano F(2024)Discovering Denial Constraints Based on Deep Reinforcement LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679714(120-129)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679714

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