# A Framework for Extracting and Encoding Features from Object-Centric Event Data

arXiv: https://arxiv.org/abs/2209.01219 · HTML: https://mlsanity.com/paper/2209.01219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Multi-Dimensional Event Data in Graph Databases](https://mlsanity.com/paper/2005.14552/) (2020)
- [Counterfactual Explanations for Predictive Business Process Monitoring](https://mlsanity.com/paper/2202.12018/) (2022)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Precision and Fitness in Object-Centric Process Mining](https://mlsanity.com/paper/2110.05375/) (2021)
- [Defining Cases and Variants for Object-Centric Event Data](https://mlsanity.com/paper/2208.03235/) (2022)
- [Causal Process Mining from Relational Databases with Domain Knowledge](https://mlsanity.com/paper/2202.08314/) (2022)
- [OPerA: Object-Centric Performance Analysis](https://mlsanity.com/paper/2204.10662/) (2022)

## Built on by (most cited first, 3 of 3)

- [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)
- [EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction](https://mlsanity.com/paper/2607.01785/) (2026)

## Abstract (arXiv)

Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of multiple processes. As traditional process mining techniques assume events associated with exactly one object, these techniques cannot be applied to object-centric event data. To use traditional process mining techniques, the object-centric event data are flattened by removing all object references but one. The flattening process is lossy, leading to inaccurate features extracted from flattened data. Furthermore, the graph-like structure of object-centric event data is lost when flattening. In this paper, we introduce a general framework for extracting and encoding features from object-centric event data. We calculate features natively on the object-centric event data, leading to accurate measures. Furthermore, we provide three encodings for these features: tabular, sequential, and graph-based. While tabular and sequential encodings have been heavily used in process mining, the graph-based encoding is a new technique preserving the structure of the object-centric event data. We provide six use cases: a visualization and a prediction use case for each of the three encodings. We use explainable AI in the prediction use cases to show the utility of both the object-centric features and the structure of the sequential and graph-based encoding for a predictive model.