Abstract
Telecommunications networks are vital enablers of modern society. Large accidents in these networks that cause their unavailability can therefore have a severe impact on the functioning of society. Learning from these accidents can help prevent them and thus make our society more resilient. In this paper, we present an accident analysis method (TRAM) which we have developed by extending the AcciMap method and we report on its application to analyse a severe accident in a telecommunications network. We validate notation for representing and breaking positive feedback loops in a network breakdown, and we suggest a method to enhance the prioritisation of recommendations derived from our analysis. Furthermore, our research reveals that splitting the analysis based on the expertise of the method’s participants negatively impacts the efficiency of the overall process.
Original language | English |
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Title of host publication | Proceedings of the 13th International Conference on Data Science, Technology and Applications, DATA 2024 |
Editors | Elhadj Benkhelifa, Alfredo Cuzzocrea, Oleg Gusikhin, Slimane Hammoudi |
Publisher | SCITEPRESS |
Pages | 62-70 |
Number of pages | 9 |
ISBN (Electronic) | 9789897587078 |
DOIs | |
Publication status | Published - 2024 |
Event | 13th International Conference on Data Science, Technology and Applications, DATA 2024 - Dijon, France Duration: 9 Jul 2024 → 11 Jul 2024 Conference number: 13 |
Conference
Conference | 13th International Conference on Data Science, Technology and Applications, DATA 2024 |
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Abbreviated title | DATA 2024 |
Country/Territory | France |
City | Dijon |
Period | 9/07/24 → 11/07/24 |
Keywords
- Accident Analysis
- Incident Analysis
- Telecommunications