PTandLogGenerator: A Generator for Artificial Event Data.

Abstract

The empirical analysis of process discovery algorithms has \nrecently 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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 28 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.