High-Level Event Mining: A Framework

TL;DR

This paper introduces a novel framework for high-level event mining that captures holistic process behavior by analyzing temporally close, capacity-sharing events, enabling more comprehensive process insights beyond individual process instances.

Contribution

The work proposes a new method for detecting and logging high-level events, creating a high-level event log that enhances process analysis capabilities.

Findings

Automatically discovers system-level patterns like traffic and workload

Effective on both simulated and real-life data

Enables application of existing process mining techniques on high-level events

Abstract

Process mining methods often analyze processes in terms of the individual end-to-end process runs. Process behavior, however, may materialize as a general state of many involved process components, which can not be captured by looking at the individual process instances. A more holistic state of the process can be determined by looking at the events that occur close in time and share common process capacities. In this work, we conceptualize such behavior using high-level events and propose a new framework for detecting and logging such high-level events. The output of our method is a new high-level event log, which collects all generated high-level events together with the newly assigned event attributes: activity, case, and timestamp. Existing process mining techniques can then be applied on the produced high-level event log to obtain further insights. Experiments on both simulated and real-life event data show that our method is able to automatically discover how system-level patterns such as high traffic and workload emerge, propagate and dissolve throughout the process.