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Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis
Computer Science and Information Systems, Tome 23 (2026) no. 1

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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.
Keywords: Exploratory Graph Analysis, Knowledge Graph, Database and Information System Architecture, Distributed Join Algorithms, Document-oriented Database 
Georgeta Bordea; Stéphane Campinas; Matteo Catena; Renaud Delbru. Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis. Computer Science and Information Systems, Tome 23 (2026) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2026_23_1_a22/
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     author = {Georgeta Bordea and St\'ephane Campinas and Matteo Catena and Renaud Delbru},
     title = {Siren {Federate:} {Bridging} {Document,} {Relational,} and {Graph} {Models} for {Exploratory} {Graph} {Analysis}},
     journal = {Computer Science and Information Systems},
     year = {2026},
     volume = {23},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2026_23_1_a22/}
}
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