Abstract
Process mining is an approach, which can discover and improve business process through extracting knowledge from event logs created in information system. Normally, process execution data in event is supported by information system and technology. Moreover, organizations perform various business processes for serving their clients. Process mining employs event log to determine control flow, process, information and performance about the resources. The precise prediction helps the manager for handling undesired situations with more control, thus future losses can be controlled. In this research, Improved Invasive Lion Algorithm (IILA) is developed for process mining. Furthermore, bounding approach is utilized for trimming the process dimension. In addition, developed IILA is employed for executing process mining. Accordingly, the developed IILA is newly designed by integrating Improved Invasive Weed Optimization (IIWO), and the Lion Algorithm (LA). The fitness measures, like precision and replayability score are also considered for obtaining better process mining performance. However, the performance of developed IILA is evaluated with two metrics, namely replayability and precision. Hence, the developed process mining model outperformed than other existing methods with replayability and precision of 94.44% and 75 respectively.
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References
W. van der Aalst (2016) “Process Mining: Data Science in Action”
Andrews R, van Dun CG, Wynn MT, Kratsch W, Röglinger MK, ter Hofstede AH (2020) “Quality-informed semi-automated event log generation for process mining”, Decision Support Systems, vol.132, pp.113265
Caldeira J, Cardoso J, Ribeiro R, Werner C (2021) “Profiling software developers with process mining and n-gram language models”
Calvanese D, Montali M, Syamsiyah A, van der Aalst W (2016) “Ontology-driven extraction of event logs from relational databases”, International Conference on Business Process Management, pp.140–153
Choueiri AC, Sato DM, Scalabrin EE, Santos EA (2020) An extended model for remaining time prediction in manufacturing systems using process mining. J Manuf Syst 56:188–201
Data underlying the paper: Automated Discovery of Process Models from Event Logs: Review and Benchmark, taken from “https://data.4tu.nl/articles/dataset/Data_underlying_the_paper_Automated_Discovery_of_Process_Models_from_Event_Logs_Review_and_Benchmark/12712727/1”, accessed on June 2021
De Oliveira H, Augusto V, Jouaneton B, Lamarsalle L, Prodel M, Xie X (2020) Optimal process mining of timed event logs. Inf Sci 528:58–78
Guo X, Zhou M, Liu S, Qi L (2019) Lexicographic multi objective scatter search for the optimization of sequence-dependent selective disassembly subject to multi resource constraints. IEEE Trans Cybernet 50(7):3307–3317
Kratsch W, Manderscheid J, Reißner D, Röglinger M (2017) Data-driven process prioritization in process networks. Decis Support Syst 100:27–40
Li W, Zhu H, Liu W, Chen D, Jiang J, Jin Q (2018) An anti-noise process mining algorithm based on minimum spanning tree clustering. IEEE Access 6:48756–48764
Li G, de Murillas EGL, de Carvalho RM, van der Aalst W (2018) “Extracting object centric event logs to support process mining on databases”, In proceedings of International Conference on Advanced Information Systems Engineering, pp.182–199
Martin N, Pufahl L, Mannhardt F (2021) “Detection of batch activities from event logs”, Information Systems, vol.95, pp.101642
Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Des Eng 6(3):284–295
De Oliveira H, Augusto V, Jouaneton B, Lamarsalle L, Prodel M, Xie X (2020) “An optimization-based process mining approach for explainable classification of timed event logs”, In proceedings of 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp.43–48
Prodel M, Augusto V, Jouaneton B, Lamarsalle L, Xie X (2018) Optimal process mining for large and complex event logs. IEEE Trans Autom Sci Eng 15(33):1309–1325
Rajakumar BR (2014) “Lion algorithm for standard and large scale bilinear system identification: a global optimization based on Lion's social behavior”, In proceedings of 2014 IEEE congress on evolutionary computation (CEC), pp.2116–2123
Sun H, Liu W, Qi L, Du Y, Ren X, Liu X (2021) A process mining algorithm to mixed multiple-concurrency short-loop structures. Inf Sci 542:453–475
Zheng W, Du Y, Qi L, Wang L (2019) A method for repairing process models containing a choice with concurrency structure by using logic Petri nets. IEEE Access 7:13106–13120
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Neerumalla, S., Parvathy, L.R. Improved invasive weed-lion optimization-based process mining of event logs. Int J Syst Assur Eng Manag 15, 49–59 (2024). https://doi.org/10.1007/s13198-021-01599-6
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DOI: https://doi.org/10.1007/s13198-021-01599-6