Siren Federate: Bridging document, relational, and graph models for exploratory graph analysis

2025-04-10

AI Summary

To address high query latency, poor scalability, and weak multi-source coordination in interactive exploration of large-scale heterogeneous knowledge graphs, this paper proposes a unified federated query system supporting document, relational, and graph data models. Our approach introduces three key contributions: (1) a semi-join decomposition technique that effectively curbs exponential blowup of intermediate results in path queries; (2) the first integration of query plan folding, semantic caching, and adaptive query planning—collectively enhancing execution efficiency and resource utilization; and (3) a novel distributed join algorithm enabling cross-modal collaborative analysis. Experimental evaluation demonstrates near-linear scalability across three dimensions—data volume, concurrent user count, and number of compute nodes—while sustaining sub-100ms end-to-end query latency.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search
• Machine Learning: Distributed Machine Learning & Federated Learning

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
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Investigative workflows require interactive exploratory analysis on large heterogeneous knowledge graphs. Current databases show limitations in enabling such task. This paper discusses the architecture of Siren Federate, a system that efficiently supports exploratory graph analysis by bridging document-oriented, relational and graph models. Technical contributions include distributed join algorithms, adaptive query planning, query plan folding, semantic caching, and semi-join decomposition for path query. Semi-join decomposition addresses the exponential growth of intermediate results in path-based queries. Experiments show that Siren Federate exhibits low latency and scales well with the amount of data, the number of users, and the number of computing nodes.

Problem

• Enabling interactive analysis on large heterogeneous knowledge graphs
• Bridging document, relational, and graph models for exploration
• Addressing exponential intermediate results in path-based queries

Innovation

• Bridges document, relational, and graph models
• Uses distributed join and adaptive query planning
• Implements semi-join decomposition for path queries