Visualizing Trace Variants From Partially Ordered Event Data

TL;DR

This paper introduces a visualization method for process trace variants that accounts for overlapping activities by incorporating start and complete timestamps, addressing limitations of traditional total order assumptions.

Contribution

The paper presents a novel visualization approach that handles partially ordered event data by integrating multiple timestamps, improving process analysis accuracy.

Findings

Enables visualization of overlapping process activities.

Addresses limitations of total order assumptions in process mining.

Improves understanding of real-world process executions.

Abstract

Executing operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event data to gain insights that are then used to optimize processes. Visual analytics for event data are essential for the application of process mining. Visualizing unique process executions -- also called trace variants, i.e., unique sequences of executed process activities -- is a common technique implemented in many scientific and industrial process mining applications. Most existing visualizations assume a total order on the executed process activities, i.e., these techniques assume that process activities are atomic and were executed at a specific point in time. In reality, however, the executions of activities are not atomic. Multiple timestamps are recorded for an executed process activity, e.g., a start-timestamp and a complete-timestamp. Therefore, the execution of process activities may overlap and, thus, cannot be represented as a total order if more than one timestamp is to be considered. In this paper, we present a visualization approach for trace variants that incorporates start- and complete-timestamps of activities.