Discovering Graph Generating Dependencies for Property Graph Profiling

TL;DR

This paper introduces GGDMiner, an automated framework for discovering approximate Graph Generating Dependencies (GGDs) in property graphs, aiding in graph data profiling and understanding data relationships.

Contribution

The paper presents GGDMiner, a novel method for automatically discovering GGDs from graph data, including a new factorized pattern representation for efficiency.

Findings

GGDMiner effectively discovers GGDs that reveal schema-level information.

The approach reduces memory and time consumption through pattern factorization.

Discovered GGDs provide insights into graph pattern correlations.

Abstract

With the increasing use of graph-structured data, there is also increasing interest in investigating graph data dependencies and their applications, e.g., in graph data profiling. Graph Generating Dependencies (GGDs) are a class of dependencies for property graphs that can express the relation between different graph patterns and constraints based on their attribute similarities. Rich syntax and semantics of GGDs make them a good candidate for graph data profiling. Nonetheless, GGDs are difficult to define manually, especially when there are no data experts available. In this paper, we propose GGDMiner, a framework for discovering approximate GGDs from graph data automatically, with the intention of profiling graph data through GGDs for the user. GGDMiner has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. To optimize memory consumption and execution time, GGDMiner uses a factorized representation of each discovered graph pattern, called Answer Graph. Our results show that the discovered set of GGDs can give an overview about the input graph, both schema level information and also correlations between the graph patterns and attributes.