ExtGraph: A Fast Extraction Method of User-intended Graphs from a Relational Database

2025-09-22

AI Summary

To address inefficient graph data extraction and inaccurate intent matching from relational databases, this paper proposes ExtGraph—a user-intent-driven, efficient graph extraction method. ExtGraph innovatively integrates outer joins with materialized views to establish a hybrid query processing mechanism that precisely reconstructs user-specified graph structures under complex multi-table join scenarios. It employs query rewriting, precomputed materialized views, and lightweight optimization strategies to substantially reduce graph construction overhead. Experimental evaluation on TPC-DS, DBLP, and IMDb datasets demonstrates that ExtGraph achieves up to 2.78× speedup over state-of-the-art approaches, while preserving both structural integrity and semantic fidelity of the extracted graphs. This work establishes a novel, efficient, and scalable paradigm for end-to-end relational-to-graph transformation, bridging the gap between relational data management and graph analytics.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Information Extraction

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Graph analytics is widely used in many fields to analyze various complex patterns. However, in most cases, important data in companies is stored in RDBMS's, and so, it is necessary to extract graphs from relational databases to perform graph analysis. Most of the existing methods do not extract a user-intended graph since it typically requires complex join query processing. We propose an efficient graph extraction method, extit{ExtGraph}, which can extract user-intended graphs efficiently by hybrid query processing of outer join and materialized view. Through experiments using the TPC-DS, DBLP, and IMDB datasets, we have shown that extit{ExtGraph} outperforms the state-of-the-art methods up to by 2.78x in terms of graph extraction time.

Problem

• Efficiently extracting user-intended graphs from relational databases
• Overcoming complex join query processing limitations in graph extraction
• Improving graph extraction speed compared to existing state-of-the-art methods

Innovation

• Hybrid query processing with outer join
• Materialized view for efficient extraction
• Faster graph extraction from relational databases