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Discovery of Temporal Graph Functional...
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Discovery of Temporal Graph Functional Dependencies

Abstract

Temporal Graph Functional Dependencies (TGFDs) are a class of data quality rules imposing topological, attribute dependency constraints over a period of time. To make TGFDs usable in practice, we study the TGFD discovery problem, and show the satisfiability, implication, and validation problems for k-bounded TGFDs are in PTIME. We introduce the TGFDMiner algorithm, which discovers minimal, frequent TGFDs. Our evaluation shows the efficiency and effectiveness of TGFDMiner, and the utility of TGFDs.

Authors

Noronha L; Chiang F

Pagination

pp. 3348-3352

Publisher

Association for Computing Machinery (ACM)

Publication Date

October 26, 2021

DOI

10.1145/3459637.3482087

Name of conference

Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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