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Investigative workflows requires 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 the document-oriented and relational models. Technical contributions include distributed join algorithms, adaptive query planning, query plan folding, and semantic caching. 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.

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Siren Federate: Bridging the Gap Between Document and Relational Data Systems for Efficient Exploratory Graph Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Stéphane Campinas,
  • Matteo Catena,
  • Renaud Delbru

摘要

Investigative workflows requires 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 the document-oriented and relational models. Technical contributions include distributed join algorithms, adaptive query planning, query plan folding, and semantic caching. 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.