Graph-centric Cross-model Data Integration and Analytics in a Unified Multi-model Database

2026-03-02

AI Summary

Existing hybrid-model databases struggle to efficiently support graph-centric cross-model data integration and analysis due to the absence of global optimization and high-performance execution mechanisms. This work proposes GredoDB, a natively unified multi-model database that seamlessly integrates graph, relational, and document data models. GredoDB introduces three key innovations: topology- and attribute-aware graph operators, a unified cross-model query optimization framework, and a parallel, operator-level execution architecture with intermediate result materialization. Evaluated on the M2Bench benchmark, GredoDB achieves an average speedup of 10.89× (up to 107.89×) on data integration tasks and 37.79× (up to 356.72×) on analytical workloads, substantially overcoming the performance limitations of conventional multi-model databases.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Graph-centric cross-model data integration and analytics (GCDIA) refer to tasks that leverage the graph model as a central paradigm to integrate relevant information across heterogeneous data models, such as relational and document, and subsequently perform complex analytics such as regression and similarity computation. As modern applications generate increasingly diverse data and move beyond simple retrieval toward advanced analytical objectives (e.g., prediction and recommendation), GCDIA has become increasingly important. Existing multi-model databases (MMDBs) struggle to efficiently support both integration (GCDI) and analytics (GCDA) in GCDIA. They typically separate graph processing from other models without global optimization for GCDI, while relying on tuple-at-a-time execution for GCDA, leading to limited performance and scalability. To address these limitations, we propose GredoDB, a unified MMDB that natively supports storing graph, relational, and document models, while efficiently processing GCDIA. Specifically, we design 1) topology- and attribute-aware graph operators for efficient predicate-aware traversal, 2) a unified GCDI optimization framework to exploit cross-model correlations, and 3) a parallel GCDA architecture that materializes intermediate results for operator-level execution. Experiments on the widely adopted multi-model benchmark M2Bench demonstrate that, in terms of response time, GredoDB achieves up to 107.89 times and an average of 10.89 times speedup on GCDI, and up to 356.72 times and an average of 37.79 times on GCDA, compared to state-of-the-art (SOTA) MMDBs.

Problem

• graph-centric
• cross-model data integration
• multi-model database
• data analytics
• performance scalability

Innovation

• graph-centric integration
• multi-model database
• cross-model optimization
• parallel graph analytics
• unified data model