Process Simulation and Pattern Discovery through
|ชื่อผู้วิจัย||Wichian Premchaiswadi, Parham Porouhan|
|สาขา||Graduate School of Information Technology|
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.
Process Mining, Model Discovery, Alpha algorithm, Heuristic Miner algorithm, Process Simulation, ProM, Bottleneck Mining, Conformance Checker, Performance Analysis with Petri net, MXML.