DOI : https://doi.org/10.18517/ijaseit.11.1.11544

Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: a Case Study

Rachmadita Andreswari (1), Ismail Syahputra (2), Muharman Lubis (3)
(1) Information System, Telkom University, Ters. Buah Batu No.1, Bandung, 40257, Indonesia
(2) Information System, Widyatama University, Jl. Cikutra No.204A, Bandung, 40125, Indonesia
(3) Information System, Telkom University, Ters. Buah Batu No.1, Bandung, 40257, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Andreswari, Rachmadita, et al. “Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: A Case Study”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 393-9, doi:10.18517/ijaseit.11.1.11544.
Citation Format :
Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

C. dos S. Garcia et al., “Process Mining Techniques and Applications - A SystematicMapping Study,” Expert Syst. Appl., vol. 133, pp. 260-295, 2019, doi: 10.1016/j.eswa.2019.05.003.

X. Zhang, Y. Du, L. Qi, and H. Sun, “An Approach for Repairing Process Models Based on Logic Petri Nets,” IEEE Accsess, vol. 6, pp. 29926-29939, 2018, doi: 10.1109/ACCESS.2018.2843137.

W. Li, Y. Fan, W. Liu, M. Xin, H. Wang, and Q. Jin, “A Self-Adaptive Process Mining Algorithm Based on Information Entropy to Deal with Uncertain Data,” IEEE Access, vol. 7, pp. 131681-131691, 2019, doi: 10.1109/ACCESS.2019.2939565.

X. Zhang, Y. Du, L. Qi, and H. Sun, “Repairing Process Models Containing Choice Structures via Logic Petri Nets,” IEEE Access, vol. 6, pp. 53796-53810, 2018, doi: 10.1109/ACCESS.2018.2870727.

Y. zhang, Y. Zhang, S. Wang, and J. Lu, “Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-Memory OLAP,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 9, pp. 1722-1735, 2019, doi: 10.1109/TKDE.2018.2867522.

A. Vaisman and E. Zimanyi, Data Warehouse Systems Design and Implementation, 1st ed. Berlin, Heidelberg: Springer, 2016.

F. Davardoost, A. Babazadeh Sangar, and K. Majidzadeh, “Extracting OLAP Cubes from Document-Oriented NoSQL Database Based on Parallel Similarity Algorithms,” Can. J. Electr. Comput. Eng., vol. 43, no. 2, pp. 111-118, 2020, doi: 10.1109/CJECE.2019.2953049.

M. R. Llave, “Business Intelligence and Analytics in Small and Medium-sized Enterprises: A Systematic Literature Review,” Procedia Comput. Sci., vol. 121, pp. 194-205, 2017, doi: https://doi.org/10.1016/j.procs.2017.11.027.

W. Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, vol. 136. 2011.

W. M. P. Van Der Aalst, “Process Cubes: Slicing, Dicing, Rolling Up and Dilling Down Event Data for Process Mining,” in Asia Pacific Business Process Management, 2017, pp. 1-22, doi: 10.1007/978-3-319-02922-1_1.

W. M. P. Van Der Aalst, “Process Cube: Turning Data into Value,” 2014.

M. Gupta and A. Sureka, “Process Cube for Software Defect Resolution,” in 2014 21st Asia-Pacific Software Engineering Conference, 2014, vol. 1, pp. 239-246, doi: 10.1109/APSEC.2014.45.

A. Bolt and W. M. P. Aalst, van der, “Multidimensional Process Mining Using Process Cubes,” in Enterprise, Business-Process and Information Systems Modeling (16th International Conference, BPMDS 2015, 20th International Conference, EMMSAD 2015, Held at CAiSE 2015, Stockholm, Sweden, June 8-9, 2015, Proceedings), 2015, pp. 102-116, doi: 10.1007/978-3-319-19237-6_7.

T. Vogelgesang and H. J. Appelrath, “Multidimensional Process Mining with PMCube Explorer,” CEUR Workshop Proc., vol. 1418, pp. 90-94, 2015.

T. Vogelgesang, S. Rinderle-Ma, and H.-J. Appelrath, “A Framework for Interactive Multidimensional Process Mining,” in Business Process Management Workshops, 2017, pp. 23-35.

T. Vogelgesang, G. Kaes, S. Rinderle-Ma, and H.-J. Appelrath, “Multidimensional Process Mining: Questions, Requirements, and Limitations,” in CAISE 2016 Forum, 2016, pp. 169-176.

P. Weber, B. Bordbar, and P. Tino, “A Framework for the Analysis of Process Mining Algorithms,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 43, no. 2, pp. 303-317, Mar. 2013, doi: 10.1109/TSMCA.2012.2195169.

A. J. M. M. Weijters, W. M. P. Aalst, van der, and A. K. Alves De Medeiros, Process Mining with The Heuristics Miner Algorithm. Technische Universiteit Eindhoven, 2006.

R. Andreswari and M. Er, “Analisis Kinerja Algoritma Penggalian Proses untuk Pemodelan Proses Bisnis Perencanaan Produksi dan Pengadaan Material pada PT.XYZ dengan Kriteria Control-Flow,” J. SISFO, vol. 5 No.1, pp. 1-8, 2014, doi: 10.24089/j.sisfo.2014.03.008.

W. van der Aalst, T. Weijters, and L. Maruster, “Workflow Mining: Discovering Process Models from Event Logs,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 9, pp. 1128-1142, 2004, doi: 10.1109/TKDE.2004.47.

A. Rozinat and W. Aalst, “Conformance Checking of Processes Based on Monitoring Real Behavior,” Inf. Syst., vol. 33, pp. 64-95, 2008, doi: 10.1016/j.is.2007.07.001.

R. Andreswari and M. Rasyidi, “OLAP Cube Processing of Production Planning Real-life Event Log: A Case Study,” Telkom University, 2017.

J. De Weerdt, M. De Backer, J. Vanthienen, and B. Baesens, “A Multidimensional Quality Assessment of State-of-the-Art Process Discovery Algorithms Using Real-Life Event Logs,” Inf. Syst., vol. 37, no. 7, pp. 654-676, Nov. 2012, doi: 10.1016/j.is.2012.02.004.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).