Process Simulation and Pattern Discovery through Alpha and Heuristic Algorithms

Last modified: July 11, 2021
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Research Article: Process Simulation and Pattern Discovery through Alpha and Heuristic Algorithms
Author: Wichian Premchaiswadi, Parham Porouhan
Email: wichian@siam.edu
Department/Faculty: Graduate School of Information Technologys, Siam University, Bangkok 10160
Published: 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015)

Citation

Premchaiswadi, W. & Porouhan, P. (2015). Process simulation and pattern discovery through alpha and heuristic algorithms. In 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015) (pp. 60-66). doi: 10.1109/ICTKE.2015.7368471.


ABSTRACT

The paper is divided into two main parts. In the first part of the study, we applied two process mining discovery techniques (i.e., alpha and heuristic algorithms) on an event log previously collected from an information system during an Academic Writing (English) training course at a private university in Thailand. The event log initially consisted of 330 process instances (i.e., number of participants) and 3,326 events (i.e., number of actions/tasks) in total. Using alpha algorithm enabled us to reconstruct causality in the form of a Petri-net graph/model. By using the heuristic algorithm we could derive XOR and AND connectors in the form of a C-net. The results showed 86.36% of the applicants/participants managed to achieve the Academic Writing (English) certificate successfully, while 6.36% of them failed to achieve any certificate after a maximum number of 3 attempts to repeat the training course. Surprisingly, 7.28% of the participants neither achieved an accredited certificate nor failed the course by dropping out before ending the course training process. In the second part of the study, we used performance analysis with Petri net technique (as a process mining conformance checking approach) in order to further analyze the points of noncompliant behavior (i.e., so-called bottlenecks or points of noncompliant behavior) for every case in the collected course training log. Based on the results, we could eventually detect the existing discrepancies of the event log leading to +24 missed tokens and -24  remained tokens altogether.

Keyword: Process Mining, Model Discovery, Alpha algorithm, Heuristic Miner algorithm, Process Simulation, ProM, Bottleneck Mining, Conformance Checker, Performance Analysis with Petri net, MXML.


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