PTandLogGenerator: a Generator for Artificial Event Data

Abstract

The empirical analysis of process discovery algorithms has recently gained more attention. An important step within such an analysis is the acquisition of the appropriate test event data, i.e. event logs and reference models. This requires an implemented framework that supports the random and automated generation of event data based on user speci cations. This paper presents a tool for generating arti cial process trees and event logs that can be used to study and compare the empirical workings of process discovery algorithms. It extends current tools by giving users full control over an extensive set of process control-flow constructs included in the fi nal models and event logs. Additionally, it is integrated within the ProM framework that o ffers a plethora of process discovery algorithms and evaluation metrics which are required during empirical analysis.