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arXiv · 2504.07815

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

Georgeta Bordea·Stephane Campinas·Matteo Catena·Renaud Delbru
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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.

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Georgeta Bordea, Stephane Campinas, Matteo Catena, Renaud Delbru. 2025-04-10. Siren Federate: Bridging document, relational, and graph models for exploratory graph analysis. https://arxiv.org/abs/2504.07815

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