Visualizing Trace Variants from Partially Ordered Event Data

Lecture notes in business information processing, pp. 34–46

Abstract

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.

Authors 3

1. Daniel Schuster corresponding Aachen

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

   RWTH Aachen University, Aachen, Germany

2. Sebastiaan J. van Zelst Aachen

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

   RWTH Aachen University, Aachen, Germany

3. Wil M. P. van der Aalst Aachen

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

   RWTH Aachen University, Aachen, Germany