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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2518794 |
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| _version_ | 1839636211217465344 |
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| author | Agneza Krajna Ana Šarčević Mario Brčić Kristijan Poje |
| author_facet | Agneza Krajna Ana Šarčević Mario Brčić Kristijan Poje |
| author_sort | Agneza Krajna |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3dcff4e778c84befa0b2f53b57c656e5 |
| institution | Matheson Library |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-3dcff4e778c84befa0b2f53b57c656e52025-07-08T05:34:21ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-07-0166355957310.1080/00051144.2025.2518794Uncovering causal graphs in air traffic control communication logs for explainable root cause analysisAgneza Krajna0Ana Šarčević1Mario Brčić2Kristijan Poje3Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaDepartment of Electrical Engineering Fundamentals and Measurements, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaDepartment of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaDepartment of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaThis 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.https://www.tandfonline.com/doi/10.1080/00051144.2025.2518794Root cause analysiscausal graphprocess miningcausal discoverycausal inferenceexplainability |
| spellingShingle | Agneza Krajna Ana Šarčević Mario Brčić Kristijan Poje Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis Automatika Root cause analysis causal graph process mining causal discovery causal inference explainability |
| title | Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| title_full | Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| title_fullStr | Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| title_full_unstemmed | Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| title_short | Uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| title_sort | uncovering causal graphs in air traffic control communication logs for explainable root cause analysis |
| topic | Root cause analysis causal graph process mining causal discovery causal inference explainability |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2518794 |
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