Parallel Flexible Heuristic Miner for Process Discovery

Process discovery aims to construct a business process model by extracting valuable information from event logs. To extract process models from event logs, it is essential to have a stable and scalable implementation of the computationally demanding process discovery techniques through parallel comp...

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Veröffentlicht in:SN computer science Jg. 4; H. 5; S. 524
Hauptverfasser: Sahu, Muktikanta, Lunia, Palak, Mohanty, Sachi Nandan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.09.2023
Springer Nature B.V
Schlagworte:
Algorithms
Application programming interface
Business process engineering
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data mining
Data Structures and Information Theory
Heuristic
Information Systems and Communication Service
Literature reviews
Methods
Original Research
Parallel programming
Pattern Recognition and Graphics
Research Trends in Communication and Network Technologies
Software Engineering/Programming and Operating Systems
Vision
Speedup
Process Model Discovery
FHM
OpenMP
Process Mining
ISSN:2661-8907, 2662-995X, 2661-8907
Online-Zugang:Volltext
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Zusammenfassung:Process discovery aims to construct a business process model by extracting valuable information from event logs. To extract process models from event logs, it is essential to have a stable and scalable implementation of the computationally demanding process discovery techniques through parallel computing. This is because the amount of data contained in event logs is growing at an exponential rate. The current study bases its computing strategy on this subject and suggests using the OpenMP Application Programming Interface (API) to construct the Flexible Heuristic Miner (FHM) algorithm for process discovery. The performance of the suggested approach is evaluated based on the speedup factor through a number of different experiments. This was accomplished by conducting an in-depth analysis of the steps that are involved in the FHM algorithm. As a result of this analysis, a suitable parallel programming framework has been proposed to reduce the execution time by making use of data and task parallelism.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01948-1