Timeline Generation from Event Logs under Evolving Properties

Proc. of the 28th Int. Symp. on Methodologies for Intelligent Systems (ISMIS). Lecture Notes in Computer Science. 2026.

When building a process model from an event log, behavioural process querying methods often focus on analysing an execution's control flow. This is because event logs typically do not encode data flow through a process explicitly. Domain knowledge about the effects of activities on objects and relations between objects can be modelled using condition-effect rules, and exploiting path querying in such rules enables us to determine the values of dynamic object attributes that are indirectly related to events. This allows us to leverage temporally local knowledge of a system's evolving data state to generate timelines from process instances. Within this setting, our main contribution is two-fold: (i) We enrich previous models with a new form of rules that allows for dynamic attribute value changes to be computed and recorded as part of the timeline. (ii) We provide a formalization of rule semantics using a transition system, in which states represent data configurations, and transitions represent the updates to those configurations resulting from the rules' effects. Finally, we provide a formal construction that generates a timeline that is correct with respect to the aforementioned rule semantics.

@inproceedings{ISMIS-2026,
   title = "Timeline Generation from Event Logs under Evolving Properties",
   year = "2026",
   author = "Rikayan Chaki and Diego Calvanese",
   booktitle = "Proc. of the 28th Int. Symp. on Methodologies for Intelligent
Systems (ISMIS)",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
}