[TMP-037] Discovering Temporal Logic patterns as binary supervised learning
This project tackles extracting clear process models from data by treating it as a binary classification problem, allowing users to influence the model's detail and usability.
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 an “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