The content introduces GGDMiner, a framework for automatically discovering approximate GGDs from graph data to profile it effectively. The process involves pre-processing, candidate generation, and GGD extraction steps. It aims to provide insights into the relationships and attributes within property graphs.
Introduction
Graph Generating Dependencies (GGDs)
Examples of GGDs
GGDMiner Framework
Candidate Generation Algorithm
Pre-processing Step
Data Extraction Metrics
Quotations
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by Larissa C. S... om arxiv.org 03-27-2024
https://arxiv.org/pdf/2403.17082.pdfDiepere vragen