# Business Process Variant Analysis based on Mutual Fingerprints of Event Logs

arXiv: https://arxiv.org/abs/1912.10598 · HTML: https://mlsanity.com/paper/1912.10598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)

## Abstract (arXiv)

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.