Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis

Georgeta Bordea1, Stéphane Campinas2, Matteo Catena2 and Renaud Delbru2

  1. L3i, La Rochelle University - La Rochelle, France
    https://l3i.univ-larochelle.fr/
    georgeta.bordea@univ-lr.fr
  2. Siren - Galway, Ireland
    https://siren.io/
    {stephane.campinas,matteo.catena, renaud.delbru}@siren.io

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.

Key words

Exploratory Graph Analysis, Knowledge Graph, Database and Information System Architecture, Distributed Join Algorithms, Document-oriented Database

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS250401080B

Publication information

Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Bordea, G., Campinas, S., Catena, M., Delbru, R.: Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis. Computer Science and Information Systems, Vol. 23, No. 1, 475-512. (2026), https://doi.org/10.2298/CSIS250401080B