TY - GEN
T1 - A Domain Specific Object Centric Event Log for Structured Representation of Incident Data
AU - Hamdi, Ahmed Takiy Eddine
AU - Elleuch, Marwa
AU - Laga, Nassim
AU - Gaaloul, Walid
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Incident prediction is critical in industrial settings to prevent disruptions and optimize operations by anticipating failures. To this end, equipment logs are commonly utilized and converted into an XES-like log format, wherein each event is associated with a single case object (i.e., equipment) reporting its status. However, the resulting event log overlooks other data sources, such as logs of pre- and post-incident processes. These logs report activities applied to the equipment's related objects (e.g., hardware, software) in non-structred way, presenting challenges when using the XES-like log format. This paper introduces a meta-model to integrate and represent these diverse data sources in formalized format. To this end, we propose a domain-specific object log tailored to the incident-monitoring domain to represent multiple equipment-related objects. This meta-model was validated with a real-life dataset, showing how it provides an effective and structured representation of incident-related data.
AB - Incident prediction is critical in industrial settings to prevent disruptions and optimize operations by anticipating failures. To this end, equipment logs are commonly utilized and converted into an XES-like log format, wherein each event is associated with a single case object (i.e., equipment) reporting its status. However, the resulting event log overlooks other data sources, such as logs of pre- and post-incident processes. These logs report activities applied to the equipment's related objects (e.g., hardware, software) in non-structred way, presenting challenges when using the XES-like log format. This paper introduces a meta-model to integrate and represent these diverse data sources in formalized format. To this end, we propose a domain-specific object log tailored to the incident-monitoring domain to represent multiple equipment-related objects. This meta-model was validated with a real-life dataset, showing how it provides an effective and structured representation of incident-related data.
KW - equipement maintenance
KW - incident prediction
KW - meta model
KW - object log
KW - unstructured data
UR - https://www.scopus.com/pages/publications/105015976408
U2 - 10.1109/SSE67621.2025.00030
DO - 10.1109/SSE67621.2025.00030
M3 - Conference contribution
AN - SCOPUS:105015976408
T3 - Proceedings - 2025 IEEE International Conference on Software Services Engineering, SSE 2025
SP - 182
EP - 188
BT - Proceedings - 2025 IEEE International Conference on Software Services Engineering, SSE 2025
A2 - Chang, Rong N.
A2 - Chang, Carl K.
A2 - Yang, Jingwei
A2 - Atukorala, Nimanthi
A2 - Chen, Dan
A2 - Helal, Sumi
A2 - Tarkoma, Sasu
A2 - He, Qiang
A2 - Kosar, Tevfik
A2 - Ardagna, Claudio
A2 - Berrocal, Javier
A2 - El Maghaouri, Kaoutar
A2 - Sun, Yanchun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Software Services Engineering, SSE 2025
Y2 - 7 July 2025 through 12 July 2025
ER -