Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis
This paper presents a novel approach for identifying system topology and detecting causal relationships between servers in Air Traffic Control systems (ATC) by utilizing unstructured, raw communication logs. We have developed a hybrid approach that combines process mining techniques, in particular t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-07-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2518794 |
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| Summary: | This paper presents a novel approach for identifying system topology and detecting causal relationships between servers in Air Traffic Control systems (ATC) by utilizing unstructured, raw communication logs. We have developed a hybrid approach that combines process mining techniques, in particular the Heuristic Miner algorithm for initial graph construction, with statistical filtering methods to improve analysis accuracy. The resulting Directed Acyclic Graph (DAG) enables the application of causal inference techniques that provide an understanding of server connections, improve the explainability of the analysis, and facilitate the identification of root causes. The proposed methodology was tested on both synthetic and real data and showed promising results in analysing causal relationships in systems with raw and unstructured logs. |
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| ISSN: | 0005-1144 1848-3380 |