# Synchronizing Process Model and Event Abstraction for Grounded Process Intelligence (Extended Version)

arXiv: https://arxiv.org/abs/2505.23536 · HTML: https://mlsanity.com/paper/2505.23536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 3 of 3)

- [Kendall Tau Sequence Distance: Extending Kendall Tau from Ranks to Sequences](https://mlsanity.com/paper/1905.02752/) (2019)
- [Discovery and Simulation of Data-Aware Business Processes](https://mlsanity.com/paper/2408.13666/) (2024)
- [Log Optimization Simplification Method for Predicting Remaining Time](https://mlsanity.com/paper/2503.07683/) (2025)

## Abstract (arXiv)

Model abstraction (MA) and event abstraction (EA) are means to reduce complexity of (discovered) models and event data. Imagine a process intelligence project that aims to analyze a model discovered from event data which is further abstracted, possibly multiple times, to reach optimality goals, e.g., reducing model size. So far, after discovering the model, there is no technique that enables the synchronized abstraction of the underlying event log. This results in loosing the grounding in the real-world behavior contained in the log and, in turn, restricts analysis insights. Hence, in this work, we provide the formal basis for synchronized model and event abstraction, i.e., we prove that abstracting a process model by MA and discovering a process model from an abstracted event log yields an equivalent process model. We prove the feasibility of our approach based on behavioral profile abstraction as non-order preserving MA technique, resulting in a novel EA technique.