Paper Info
Title
Incremental Process Discovery using Petri Net Synthesis.
Abstract
Process discovery aims at constructing a model from a set of observations given by execution traces (a log). Petri nets are a preferred target model in that they produce a compact description of the system by exhibiting its concurrency. This article presents a process discovery algorithm using Petri net synthesis, based on the notion of region introduced by A. Ehrenfeucht and G. Rozenberg and using techniques from linear algebra. The algorithm proceeds in three successive phases which make it possible to find a compromise between the ability to infer behaviours of the system from the set of observations while ensuring a parsimonious model, in terms of fitness, precision and simplicity. All used algorithms are incremental which means that one can modify the produced model when new observations are reported without reconstructing the model from scratch.
Year
DOI
Venue
2017
10.3233/FI-2017-1548
FUNDAMENTA INFORMATICAE
Keywords
Field
DocType
Region theory,Petri net synthesis,process discovery
Discrete mathematics,Linear algebra,Petri net,Computer science,Concurrency,Process architecture,Stochastic Petri net,Theoretical computer science,Artificial intelligence,Business process discovery
Journal
Volume
Issue
ISSN
154
1-4
0169-2968
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Eric Badouel143731.67
Uli Schlachter2265.95