Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis.
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| 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] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192054655 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bordea%2C+Georgeta%22">Bordea, Georgeta</searchLink><relatesTo>1</relatesTo><i> georgeta.bordea@univ-lr.fr</i><br /><searchLink fieldCode="AR" term="%22Campinas%2C+Stéphane%22">Campinas, Stéphane</searchLink><relatesTo>2</relatesTo><i> stephane.campinas@siren.io</i><br /><searchLink fieldCode="AR" term="%22Catena%2C+Matteo%22">Catena, Matteo</searchLink><relatesTo>2</relatesTo><i> matteo.catena@siren.io</i><br /><searchLink fieldCode="AR" term="%22Delbru%2C+Renaud%22">Delbru, Renaud</searchLink><relatesTo>2</relatesTo><i> renaud.delbru@siren.io</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jan2026, Vol. 23 Issue 1, p475-512. 38p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Relational+databases%22">Relational databases</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Nonrelational+databases%22">Nonrelational databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS250401080B Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 475 Subjects: – SubjectFull: Relational databases Type: general – SubjectFull: Knowledge graphs Type: general – SubjectFull: Nonrelational databases Type: general Titles: – TitleFull: Siren Federate: Bridging Document, Relational, and Graph Models for Exploratory Graph Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bordea, Georgeta – PersonEntity: Name: NameFull: Campinas, Stéphane – PersonEntity: Name: NameFull: Catena, Matteo – PersonEntity: Name: NameFull: Delbru, Renaud IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: Computer Science & Information Systems Type: main |
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