# Defining Cases and Variants for Object-Centric Event Data

arXiv: https://arxiv.org/abs/2208.03235 · HTML: https://mlsanity.com/paper/2208.03235/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [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)
- [Causal Process Mining from Relational Databases with Domain Knowledge](https://mlsanity.com/paper/2202.08314/) (2022)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [Recent Advances on the Graph Isomorphism Problem](https://mlsanity.com/paper/2011.01366/) (2020)
- [OPerA: Object-Centric Performance Analysis](https://mlsanity.com/paper/2204.10662/) (2022)
- [Cortado---An Interactive Tool for Data-Driven Process Discovery and Modeling](https://mlsanity.com/paper/2105.07666/) (2021)

## Built on by (most cited first, 8 of 8)

- [A Framework for Extracting and Encoding Features from Object-Centric Event Data](https://mlsanity.com/paper/2209.01219/) (2022)
- [HOEG: A New Approach for Object-Centric Predictive Process Monitoring](https://mlsanity.com/paper/2404.05316/) (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)
- [INEXA: Interactive and Explainable Process Model Abstraction Through Object-Centric Process Mining](https://mlsanity.com/paper/2403.18659/) (2024)
- [Posets and Bounded Probabilities for Discovering Order-inducing Features in Event Knowledge Graphs](https://mlsanity.com/paper/2410.06065/) (2024)
- [Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail](https://mlsanity.com/paper/2509.21322/) (2025)
- [Resolution limits for process comparison from event data](https://mlsanity.com/paper/2609.20489/) (2026)

## Abstract (arXiv)

The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining techniques, one has to associate each event with exactly one object, e.g., the company's customer. Events related to one object form an event sequence called a case. A case describes an end-to-end run through a process. The cases contained in event data can be used to discover a process model, detect frequent bottlenecks, or learn predictive models. However, events encountered in real-life information systems, e.g., ERP systems, can often be associated with multiple objects. The traditional sequential case concept falls short of these object-centric event data as these data exhibit a graph structure. One might force object-centric event data into the traditional case concept by flattening it. However, flattening manipulates the data and removes information. Therefore, a concept analogous to the case concept of traditional event logs is necessary to enable the application of different process mining tasks on object-centric event data. In this paper, we introduce the case concept for object-centric process mining: process executions. These are graph-based generalizations of cases as considered in traditional process mining. Furthermore, we provide techniques to extract process executions. Based on these executions, we determine equivalent process behavior with respect to an attribute using graph isomorphism. Equivalent process executions with respect to the event's activity are object-centric variants, i.e., a generalization of variants in traditional process mining. We provide a visualization technique for object-centric variants. The contribution's scalability and efficiency are extensively evaluated. Furthermore, we provide a case study showing the most frequent object-centric variants of a real-life event log.