Making sense of data is a main challenge in creating human understandable descriptions of complex situations. When data refer to process executions, techniques exist that discover explicit descriptions in terms of formal models. Many research works envisage the discovery task as a one-class supervised learning job. Work on deviance mining highlighted nonetheless the need to characterise behaviours that exhibit certain characteristics and forbid others (e.g., the slower, less frequent), leading to the quest for a binary supervised learning task.

In this microproject we focus on the discovery of declarative process models, expressed through Linear Time Temporal Logic, as a binary supervised learning task, where the input log reports both positive and negative behaviours. We therefore investigate how valuable information can be extracted and formalised into a “optimal” model, according to user-preferences (e.g., model generality or simplicity). By iteratively including further examples, the user can also refine the discovered models

Output

Paper to be submitted to relevant journal

Machine learning tool

Artificial data set

Project Partners:

  • Fondazione Bruno Kessler (FBK), Chiara Ghidini
  • Università di Bologna (UNIBO), Federico Chesani

Primary Contact: Chiara Ghidini, FBK