Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/27485
Title: bupaR: Enabling reproducible business process analysis
Authors: JANSSENSWILLEN, Gert 
DEPAIRE, Benoit 
SWENNEN, Marijke 
JANS, Mieke 
VANHOOF, Koen 
Issue Date: 2018
Source: Knowledge-based systems, 163, p. 927-930
Abstract: Over the last decades, the field of process mining has emerged as a response to a growing amount of event data being recorded in the context of business processes. Concurrently with the increasing amount of literature produced in this field, a set of tools has been developed to implement the various algorithms and provide them to end users. However, the majority of tools does not provide the possibility of creating workflows which can be reused at a later point in time to reproduce the results, and most tools are not easily customizable. This paper introduces bupaR, an integrated collection of R-packages which creates a framework for reproducible process analysis in R and supports different steps of a process analysis project, from data extraction to data analysis. It is an extensible framework of several R-packages to analyze process data, each with their specific purpose and set of tools.
Notes: Janssenswillen, G (reprint author), UHasselt Hasselt Univ, Fac Business Econ, Martelarenlaan 42, B-3500 Hasselt, Belgium. gert.janssenswillen@uhasselt.be
Keywords: Event data; Process analysis; R; bupaR; edeaR; eventdataR; processmapR; processmonitR; xesreadR
Document URI: http://hdl.handle.net/1942/27485
ISSN: 0950-7051
e-ISSN: 1872-7409
DOI: 10.1016/j.knosys.2018.10.018
ISI #: 000454468200073
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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