# Process discovery on deviant traces and other stranger things

arXiv: https://arxiv.org/abs/2109.14883 · HTML: https://mlsanity.com/paper/2109.14883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 96 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [A Subsequence Interleaving Model for Sequential Pattern Mining](https://mlsanity.com/paper/1602.05012/) (2016)
- [Business Process Deviance Mining: Review and Evaluation](https://mlsanity.com/paper/1608.08252/) (2016)
- [Parallelizing Machine Learning as a Service for the End-User](https://mlsanity.com/paper/2005.14080/) (2020)
- [Unfolding-Based Process Discovery](https://mlsanity.com/paper/1507.02744/) (2015)

## Built on by (most cited first, 2 of 2)

- [Direct Encoding of Declare Constraints in ASP](https://mlsanity.com/paper/2412.10152/) (2024)
- [Discriminative Rule Learning for Outcome-Guided Process Model Discovery](https://mlsanity.com/paper/2510.27343/) (2025)

## Abstract (arXiv)

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing "stranger" behaviours according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is "optimal" according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results as regards both the performance and the quality of the obtained solution.