Identifying the Key Attributes in an Unlabeled Event Log for Automated Process Discovery

TL;DR

This paper presents a two-stage machine learning approach to automatically identify key attributes in unlabeled event logs, significantly reducing manual effort and computational complexity for process discovery.

Contribution

The authors propose a novel two-stage method combining supervised learning and process model evaluation to automate key attribute identification in event logs, enhancing process mining automation.

Findings

Method reduces computational complexity from O(N^3) to O(k^3).

Successfully identified key attributes in 14 datasets within 20 seconds.

Effective even with only 2 candidate attributes in the first stage.

Abstract

Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user needs to specify them manually, and this can be an exhaustive task. In this paper, we propose a two-stage key attribute identification method to avoid such a manual investigation, and thus this is a step toward fully automated process discovery. One of the challenging tasks is how to avoid exhaustive computation due to combinatorial explosion. For this, we narrow down candidates for each key attribute by using supervised machine learning in the first stage and identify the best combination of the key attributes by discovering process models and evaluating them in the second stage. Our computational complexity can be reduced from $\mathcal{O}(N^3)$ to $\mathcal{O}(k^3)$ where $N$ and $k$ are the numbers of columns and candidates we keep in the first stage, respectively, and usually $k$ is much smaller than $N$. We evaluated our method with 14 open datasets and showed that our method could identify the key attributes even with $k = 2$ for about 20 seconds for many datasets.