Process mining: software comparison, trends, and challenges
1
Stratesys, Madrid, Spain
|
Publication type: Journal Article
Publication date: 2022-12-30
scimago Q2
wos Q2
SJR: 0.678
CiteScore: 9.2
Impact factor: 2.8
ISSN: 2364415X, 23644168
Computer Science Applications
Computational Theory and Mathematics
Information Systems
Applied Mathematics
Modeling and Simulation
Abstract
Process mining is the confluence between data mining and business process management, which is a growing and promising research topic. From process execution event logs, process mining focuses on understanding end-to-end processes and helps provide more significant findings. In this paper, a brief review of each of the main stages (discovery, conformance, and enhancement) of the process mining and low-code automation platforms for business processes are stated. Also, it provides an analysis of the 16 most prominent process mining software as well as an in-depth taxonomy considering 55 features. From this comparison, a subset of software obtained the best scores for process discovery while others for process simulation. Finally, trends and a set of challenges for process mining are pointed out.
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Metrics
13
Total citations:
13
Citations from 2024:
10
(76.92%)
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GOST
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Loyola-González O. Process mining: software comparison, trends, and challenges // International Journal of Data Science and Analytics. 2022.
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Loyola-González O. Process mining: software comparison, trends, and challenges // International Journal of Data Science and Analytics. 2022.
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RIS
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TY - JOUR
DO - 10.1007/s41060-022-00379-0
UR - https://doi.org/10.1007/s41060-022-00379-0
TI - Process mining: software comparison, trends, and challenges
T2 - International Journal of Data Science and Analytics
AU - Loyola-González, Octavio
PY - 2022
DA - 2022/12/30
PB - Springer Nature
SN - 2364-415X
SN - 2364-4168
ER -
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BibTex (up to 50 authors)
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@article{2022_Loyola-González,
author = {Octavio Loyola-González},
title = {Process mining: software comparison, trends, and challenges},
journal = {International Journal of Data Science and Analytics},
year = {2022},
publisher = {Springer Nature},
month = {dec},
url = {https://doi.org/10.1007/s41060-022-00379-0},
doi = {10.1007/s41060-022-00379-0}
}