Open AccessDiscovering generative models from event logs: data-driven simulation vs deep learning
Author(s) -
Manuel Camargo,
Marlon Dumas,
Oscar González-Rojas
Publication year - 2021
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.577
Subject(s) - generative grammar , computer science , generative model , event (particle physics) , artificial intelligence , context (archaeology) , machine learning , process (computing) , set (abstract data type) , deep learning , generative design , process modeling , data science , work in process , engineering , paleontology , metric (unit) , operations management , physics , quantum mechanics , biology , programming language , operating system
A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.