Enhancing Municipal Revenue Processes: A Case Study on Process Mining Approaches in Hamedan Province

Subject Areas : International Journal of Decision Intelligence

Mohammad Amin Jafari ¹ , Mansour Esmaeilpour ²

1 -
2 -

Received: 2024-09-23 Accepted : 2025-01-29 Published : 2025-02-04

Keywords: Process Mining, Event Log Analysis, Hamedan Municipality, Process Model Enhancement,

Abstract :

Process mining has gained significant attention in recent years as a method for analyzing and improving business processes through the examination of event data. Unlike traditional data mining, which focuses on isolated tasks, process mining provides a comprehensive view of entire processes, helping organizations understand how work is actually carried out. With the increasing availability of event data and advances in process discovery techniques, process mining has become an essential tool in Business Process Management (BPM) and Workflow Management (WFM) systems. This study evaluates four process mining algorithms: Alpha Miner, Alpha++ Miner, Genetic Miner, and Inductive Miner, in the context of enhancing the revenue processes of Hamadan’s municipality. The goal is to improve efficiency, reduce delays, and optimize resource allocation in the municipality’s revenue system. The results show that the Genetic Miner delivers the highest overall performance with an impressive score of 98%, followed by Inductive Miner at 96%, Alpha++ Miner at 94%, and Alpha Miner at 83%. The Genetic Miner’s impact on the municipality’s operations was particularly striking, leading to a 40% increase in revenue growth, a 33.3% reduction in revenue leakage, and a 50% improvement in payment processing time. These findings illustrate the power of process mining, particularly Genetic Miner, to reveal inefficiencies and enhance decision-making, ultimately contributing to better public service and more effective resource management. This research underscores the potential of process mining to transform municipal revenue processes, driving improvements in governance and operational efficiency.

References:

[1] Zerbino, P., Stefanini, A., & Aloini, D. (2021).
Process science in action: A literature review on
process mining in business management.
Technological Forecasting and Social Change, 172,
121021.
[2] De Weerdt, J., & Wynn, M. T. (2022). Foundations
of process event data. In Process Mining Handbook
(pp. 193-211). Cham: Springer International
Publishing.
[3] dos Santos Garcia, C., Meincheim, A., Junior, E. R.
F., Dallagassa, M. R., Sato, D. M. V., Carvalho, D.
R., ... & Scalabrin, E. E. (2019). Process mining
techniques and applications–A systematic mapping
study. Expert Systems with Applications, 133, 260-
295.
[4] Dakic, D., Stefanovic, D., Lolic, T., Narandzic, D.,
& Simeunovic, N. (2020). Event log extraction for
the purpose of process mining: A systematic
literature review. In Innovation in Sustainable
Management and Entrepreneurship: 2019
International Symposium in Management
(SIM2019) (pp. 299-312). Springer International
Publishing.
[5] Van Der Aalst, W., & van der Aalst, W. (2016). Data
science in action (pp. 3-23). Springer Berlin
Heidelberg.
[6] https://apromore.com/process-mining-101
[7] Juhaňák, L., Zounek, J., & Rohlíková, L. (2019).
Using process mining to analyze students' quiztaking behavior patterns in a learning management
system. Computers in Human Behavior, 92, 496-
506.
[8] Bădică, A., Bădică, C., Buligiu, I., & Ciora, L. I.
(2022). Exploring the Usability of Process Mining
in Smart City. IFAC-PapersOnLine, 55(11), 42-47.
[9] Saint, J., Fan, Y., Singh, S., Gasevic, D., & Pardo,
A. (2021, April). Using process mining to analyse
self-regulated learning: a systematic analysis of four
algorithms. In LAK21: 11th international learning
analytics and knowledge conference (pp. 333-343).
[10] Theis, J., Galanter, W. L., Boyd, A. D., & Darabi,
H. (2021). Improving the in-hospital mortality
prediction of diabetes ICU patients using a process
mining/deep learning architecture. IEEE Journal of
Biomedical and Health Informatics, 26(1), 388-399.
[11] Nafasa, P., Waspada, I., Bahtiar, N., & Wibowo, A.
(2019, October). Implementation of alpha miner
algorithm in process mining application
development for online learning activities based on
moodle event log data. In 2019 3rd International
Conference on Informatics and Computational
Sciences (ICICoS) (pp. 1-6). IEEE.
[12] https://en.wikipedia.org/wiki/Alpha_algorithm
[13] Effendi, Y. A., & Sarno, R. (2018, September).
Conformance checking evaluation of process
discovery using modified alpha++ miner algorithm.
In 2018 International Seminar on Application for
Technology of Information and Communication
(pp. 435-440). IEEE.
[14] de Medeiros, A. K. A., Weijters, A. J., & van der
Aalst, W. M. (2007). Genetic process mining: an
experimental evaluation. Data mining and
knowledge discovery, 14, 245-304.
[15] Gupta, E. P. (2014). Process mining a comparative
study. International Journal of Advanced Research
in Computer and Communications Engineering,
3(11), 5.
[16] Pulsanong, W., Porouhan, P., Tumswadi, S., &
Premchaiswadi, W. (2017, November). Using
inductive miner to find the most optimized path of
workflow process. In 2017 15th International
Conference on ICT and Knowledge Engineering
(ICT&KE) (pp. 1-5). IEEE.
[17] Buijs, J. C., van Dongen, B. F., & van der Aalst, W.
M. (2014). Quality dimensions in process discovery:
The importance of fitness, precision, generalization
and simplicity. International Journal of Cooperative
Information Systems, 23(01), 1440001.
[18] Buijs, J. C. (2014). Flexible evolutionary algorithms
for mining structured process models.