Discovery of Temporal Graph Functional Dependencies
2021
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.
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