# Data is Moody: Discovering Data Modification Rules from Process Event Logs

arXiv: https://arxiv.org/abs/2312.14571 · HTML: https://mlsanity.com/paper/2312.14571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences](https://mlsanity.com/paper/2102.07298/) (2021)
- [Interpretable multiclass classification by MDL-based rule lists](https://mlsanity.com/paper/1905.00328/) (2019)
- [The Minimum Description Length Principle for Pattern Mining: A Survey](https://mlsanity.com/paper/2007.14009/) (2020)
- [A Survey on Concept Drift in Process Mining](https://mlsanity.com/paper/2112.02000/) (2021)
- [The Long and the Short of It: Summarising Event Sequences with Serial Episodes](https://mlsanity.com/paper/1902.02834/) (2019)
- [Process Discovery Using Graph Neural Networks](https://mlsanity.com/paper/2109.05835/) (2021)
- [Truly Unordered Probabilistic Rule Sets for Multi-class Classification](https://mlsanity.com/paper/2206.08804/) (2022)
- [Skopus: Mining top-k sequential patterns under leverage](https://mlsanity.com/paper/1506.08009/) (2015)
- [Discovering Reliable Causal Rules](https://mlsanity.com/paper/2009.02728/) (2020)
- [Robust subgroup discovery](https://mlsanity.com/paper/2103.13686/) (2021)
- [Mining Closed Episodes with Simultaneous Events](https://mlsanity.com/paper/1904.08741/) (2019)

## Abstract (arXiv)

Although event logs are a powerful source to gain insight about the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity sequences of an event log, while ignoring event attribute data. Event attribute data has mostly been used to predict event occurrences and process outcome, but the state of the art neglects to mine succinct and interpretable rules how event attribute data changes during process execution. Subgroup discovery and rule-based classification approaches lack the ability to capture the sequential dependencies present in event logs, and thus lead to unsatisfactory results with limited insight into the process behavior. Given an event log, we are interested in finding accurate yet succinct and interpretable if-then rules how the process modifies data. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we choose the model with the best lossless description of the data. Additionally, we propose the greedy Moody algorithm to efficiently search for rules. By extensive experiments on both synthetic and real-world data, we show Moody indeed finds compact and interpretable rules, needs little data for accurate discovery, and is robust to noise.