Bibliographic Details
| Title: |
Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis. |
| Authors: |
Bordea, Georgeta1 georgeta.bordea@univ-lr.fr, Campinas, Stéphane2 stephane.campinas@siren.io, Catena, Matteo2 matteo.catena@siren.io, Delbru, Renaud2 renaud.delbru@siren.io |
| Source: |
Computer Science & Information Systems. Jan2026, Vol. 23 Issue 1, p475-512. 38p. |
| Subjects: |
Relational databases, Knowledge graphs, Nonrelational databases |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
