# Exploring Business Process Deviance with Sequential and Declarative Patterns

arXiv: https://arxiv.org/abs/2111.12454 · HTML: https://mlsanity.com/paper/2111.12454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Business Process Deviance Mining: Review and Evaluation](https://mlsanity.com/paper/1608.08252/) (2016)
- [Detection of statistically significant differences between process variants through declarative rules](https://mlsanity.com/paper/2104.07926/) (2021)

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

- [Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles](https://mlsanity.com/paper/2303.14939/) (2023)
- [Towards Explainable Sequential Learning](https://mlsanity.com/paper/2505.23624/) (2025)

## Abstract (arXiv)

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing event logs stored by the systems supporting the execution of a business process. In this paper, the problem of explaining deviations in business processes is first investigated by using features based on sequential and declarative patterns, and a combination of them. Then, the explanations are further improved by leveraging the data attributes of events and traces in event logs through features based on pure data attribute values and data-aware declarative rules. The explanations characterizing the deviances are then extracted by direct and indirect methods for rule induction. Using real-life logs from multiple domains, a range of feature types and different forms of decision rules are evaluated in terms of their ability to accurately discriminate between non-deviant and deviant executions of a process as well as in terms of understandability of the final outcome returned to the users.