# OPerA: Object-Centric Performance Analysis

arXiv: https://arxiv.org/abs/2204.10662 · HTML: https://mlsanity.com/paper/2204.10662/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [Multi-Dimensional Event Data in Graph Databases](https://mlsanity.com/paper/2005.14552/) (2020)
- [Precision and Fitness in Object-Centric Process Mining](https://mlsanity.com/paper/2110.05375/) (2021)
- [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)
- [A Novel Token-Based Replay Technique to Speed Up Conformance Checking and Process Enhancement](https://mlsanity.com/paper/2007.14237/) (2020)

## Built on by (most cited first, 4 of 4)

- [Defining Cases and Variants for Object-Centric Event Data](https://mlsanity.com/paper/2208.03235/) (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)
- [Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study](https://mlsanity.com/paper/2310.10174/) (2023)

## Abstract (arXiv)

Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Such insights are reliably interpreted by process analysts in the context of a model with formal semantics. Existing techniques for performance analysis assume that a single case notion exists in a business process (e.g., a patient in healthcare process). However, in reality, different objects might interact (e.g., order, item, delivery, and invoice in an O2C process). In such a setting, traditional techniques may yield misleading or even incorrect insights on performance metrics such as waiting time. More importantly, by considering the interaction between objects, we can define object-centric performance metrics such as synchronization time, pooling time, and lagging time. In this work, we propose a novel approach to performance analysis considering multiple case notions by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.