PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings

TL;DR

This paper introduces PLG2, a comprehensive framework for generating multiperspective process models and logs, supporting online and offline scenarios, to aid in the evaluation of process mining algorithms with realistic synthetic data.

Contribution

It extends existing process generation methods by supporting multiperspective models, online event streams, and concept drifts, covering a wide range of real-world scenarios.

Findings

Supports multiperspective models with time and data

Generates online event streams with concept drifts

Provides a publicly available Java tool for process simulation

Abstract

Process mining represents an important field in BPM and data mining research. Recently, it has gained importance also for practitioners: more and more companies are creating business process intelligence solutions. The evaluation of process mining algorithms requires, as any other data mining task, the availability of large amount of real-world data. Despite the increasing availability of such datasets, they are affected by many limitations, in primis the absence of a "gold standard" (i.e., the reference model). This paper extends an approach, already available in the literature, for the generation of random processes. Novelties have been introduced throughout the work and, in particular, they involve the complete support for multiperspective models and logs (i.e., the control-flow perspective is enriched with time and data information) and for online settings (i.e., generation of multiperspective event streams and concept drifts). The proposed new framework is able to almost entirely cover the spectrum of possible scenarios that can be observed in the real-world. The proposed approach is implemented as a publicly available Java application, with a set of APIs for the programmatic execution of experiments.