# Precision and Fitness in Object-Centric Process Mining

arXiv: https://arxiv.org/abs/2110.05375 · HTML: https://mlsanity.com/paper/2110.05375/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 6 Oct 2021.

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

- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [Extracting Multiple Viewpoint Models from Relational Databases](https://mlsanity.com/paper/2001.02562/) (2020)
- [Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)](https://mlsanity.com/paper/1909.02393/) (2019)
- [Object-Centric Behavioral Constraints](https://mlsanity.com/paper/1703.05740/) (2017)
- [DB-Nets: on The Marriage of Colored Petri Nets and Relational Databases](https://mlsanity.com/paper/1611.03680/) (2016)
- [Guided Interaction Exploration in Artifact-centric Process Models](https://mlsanity.com/paper/1706.02109/) (2017)
- [Verifiable UML Artifact-Centric Business Process Models (Extended Version)](https://mlsanity.com/paper/1408.5094/) (2014)
- [The Imprecisions of Precision Measures in Process Mining](https://mlsanity.com/paper/1705.03303/) (2017)

## Built on by (most cited first, 9 of 9)

- [Defining Cases and Variants for Object-Centric Event Data](https://mlsanity.com/paper/2208.03235/) (2022)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [OPerA: Object-Centric Performance Analysis](https://mlsanity.com/paper/2204.10662/) (2022)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [A Framework for Extracting and Encoding Features from Object-Centric Event Data](https://mlsanity.com/paper/2209.01219/) (2022)
- [Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs](https://mlsanity.com/paper/2210.12080/) (2022)
- [Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives](https://mlsanity.com/paper/2407.11280/) (2024)
- [Extracting Process-Aware Decision Models from Object-Centric Process Data](https://mlsanity.com/paper/2401.14847/) (2024)
- [Predictive Process Monitoring Using Object-centric Graph Embeddings](https://mlsanity.com/paper/2507.15411/) (2025)

## Abstract (arXiv)

Traditional process mining considers only one single case notion and discovers and analyzes models based on this. However, a single case notion is often not a realistic assumption in practice. Multiple case notions might interact and influence each other in a process. Object-centric process mining introduces the techniques and concepts to handle multiple case notions. So far, such event logs have been standardized and novel process model discovery techniques were proposed. However, notions for evaluating the quality of a model are missing. These are necessary to enable future research on improving object-centric discovery and providing an objective evaluation of model quality. In this paper, we introduce a notion for the precision and fitness of an object-centric Petri net with respect to an object-centric event log. We give a formal definition and accompany this with an example. Furthermore, we provide an algorithm to calculate these quality measures. We discuss our precision and fitness notion based on an event log with different models. Our precision and fitness notions are an appropriate way to generalize quality measures to the object-centric setting since we are able to consider multiple case notions, their dependencies and their interactions.