核心概念
GGDMiner automates the discovery of Graph Generating Dependencies to profile graph data efficiently.
要約
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.
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Introduction
- Definition of Data Dependencies.
- Importance of Graph Data.
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Graph Generating Dependencies (GGDs)
- Expressive power in capturing constraints.
- Comparison with other dependencies.
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Examples of GGDs
- Constraints on relations between nodes.
- Constraints on attributes of graph patterns.
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GGDMiner Framework
- Pre-processing step for preparation.
- Candidate generation using a lattice structure.
- Utilization of Answer Graph for efficient operations.
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Candidate Generation Algorithm
- Lattice construction process.
- Vertical and horizontal expansion methods.
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Pre-processing Step
- Selection of important attributes.
- Construction of similarity indexes.
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Data Extraction Metrics
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Quotations
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統計
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引用
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