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  5. CANDI - a semantic framework for CAN bus data modeling and system integration

CANDI - a semantic framework for CAN bus data modeling and system integration

Publication date
2026-05-08
Document type
Conference paper
Author
Ivanovic, Pavle  
Burbach, Simon  
Niggemann, Oliver  
Maleshkova, Maria  
Organisational unit
High Performance Computing  
Data Engineering  
Informatik im Maschinenbau  
DTEC.bw  
DOI
10.1007/978-3-032-25159-6_8
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/23886
Conference
23rd European Semantic Web Conference (ESWC 2026) ; Dubrovnik, Croatia ; May 10–14, 2026
Project
Digitale Zwillinge für Intelligente Schiffe und für Schiffsflotten  
Publisher
Springer
Series or journal
Lecture Notes in Computer Science
Periodical volume
16550
Book title
The Semantic Web : 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik, Croatia, May 10–14, 2026, Proceedings
Volume (part of multivolume book)
2
ISBN
978-3-032-25159-6
Part of the university bibliography
✅
Additional Information
Language
English
Keyword
CAN Bus
Data Security
DBC Decoding
Deployment Automation
OBDA Framework
Ontology Engineering
Real-time Analytics
dtec.bw
Abstract
Modern automotive, maritime, railway, and airborne systems generate massive streams of operational data that challenge existing solutions for storage, security, and data management. Semantic integration techniques improve interoperability across heterogeneous sources, yet often fall short in deployment automation, scalability, and real-time operation. We present CANDI, a semantic data integration framework that enables dynamic decoding and ontological access to Controller Area Network (CAN) data. Leveraging virtual knowledge graphs, CANDI links low-level logging streams with structured semantic representations, supporting advanced diagnostics and informed decision making. The framework incorporates the DBC ontology, a CAN database extension of the W3C SSN/SOSA standards that formalizes the semantics of messages, signals, ECUs, decoding schemas, and data logging processes. Using real-world datasets, we demonstrate CANDI’s contributions to end-to-end deployment automation, runtime CAN bus decoding, and secure, semantically driven analytics on streaming telemetry. The DBC ontology is rigorously evaluated for logical consistency, domain coverage, and knowledge graph instantiation, underscoring its robustness and industrial relevance. Ontology: https://paitools.github.io/DBCOntology/DBC.owl.GitHub: https://github.com/paitools/DBCOntology.Documentation: https://w3id.org/dbc-ontology.License: https://creativecommons.org/licenses/by-nc-sa/4.0.DOI: https://doi.org/10.5281/zenodo.17671851. Ontology: https://paitools.github.io/DBCOntology/DBC.owl. GitHub: https://github.com/paitools/DBCOntology. Documentation: https://w3id.org/dbc-ontology. License: https://creativecommons.org/licenses/by-nc-sa/4.0. DOI: https://doi.org/10.5281/zenodo.17671851.
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Published version
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