[TMP-037] Discovering Temporal Logic patterns as binary supervised learning

Contact person: Chiara Ghidini (ghidini@fbk.eu) 

Internal Partners:

1. University of Bologna, Federico Chesani, fchesani@gmail.com

External Partners:

1. Free University of Bozen-Bolzano, Sergio Tessaris, tessaris@inf.unibz.it

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

Results Summary

The micro-project has produced three main results:

1. A two-step approach for the discovery of temporal-logic patterns as a binary supervised learning problem, that is starting from a set of “positive traces” (execution traces whose behaviour we want to observe in the discovered patterns), and a set of “negative traces” (execution traces whose behaviour we do not want to observe in the discovered patterns). In detail, in the first step, sets of patterns (possible models) that accept all positive traces and discard as much as possible of the negative ones are discovered. In the second step, the model(s) optimizing one criterion, as for instance the generality or the simplicity, are selected among the possible discovered models.
2. Two synthetic labelled (“positive” and “negative”) event log datasets used for the synthetic evaluation of the proposed approach.
3. A paper describing the approach, as well as the approach evaluation.

Tangible Outcomes

1. Chesani, F., Francescomarino, C. D., Ghidini, C., Grundler, G., Loreti, D., Maggi, F. M., Mello, P., Montali, M., and Tessaris, S. (2022). Shape your process: Discovering declarative business processes from positive and negative traces taking into account user preferences. In Almeida, J. P. A., Karastoyanova, D., Guizzardi, G., Montali, M., Maggi, F. M., and Fonseca, C. M., editors, Enterprise Design, Operations, and Computing – 26th International Conference, EDOC 2022, Bozen-Bolzano, Italy, October 3-7, 2022, Proceedings, volume 13585 of Lecture Notes in Computer Science, pages 217–234. Springer. https://dl.acm.org/doi/10.1007/978-3-031-17604-3_13 
2. Chesani, F., Francescomarino, C. D., Ghidini, C., Loreti, D., Maggi, F. M., Mello, P., Montali, M., and Tessaris, S. (2022). Process discovery on deviant traces and other stranger things. IEEE Transactions on Knowledge and Data Engineering, pages 1–17. DOI: https://doi.org/10.1109/TKDE.2022.3232207 https://ieeexplore.ieee.org/document/9999331
3. Loan Approval1: dataset. https://drive.google.com/drive/folders/15BwG4PJq8iIMh9Sr9dpMXAYBYqp7QDE?usp=sharing
4. Loan Approval2: dataset. https://drive.google.com/drive/folders/1fcJ8itzdMbNOjEAeV6nUEeI5B6__aB_c?usp=sharing
5. Discovery Framework (program/code): https://zenodo.org/records/5158528 
6. Experiments: https://github.com/stessaris/negdis-experiments/tree/v1.0