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Khodayari Tehraninejad, Dina, Khoshnevis, Sedigheh. (1400). Proxima: Process Mining for Extracting Configurable Process Models Using Software Product Line Concepts. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 9(1), 1-29. doi: 10.22108/jcs.2021.130065.1080
Dina Khodayari Tehraninejad; Sedigheh Khoshnevis. "Proxima: Process Mining for Extracting Configurable Process Models Using Software Product Line Concepts". سامانه مدیریت نشریات علمی دانشگاه اصفهان, 9, 1, 1400, 1-29. doi: 10.22108/jcs.2021.130065.1080
Khodayari Tehraninejad, Dina, Khoshnevis, Sedigheh. (1400). 'Proxima: Process Mining for Extracting Configurable Process Models Using Software Product Line Concepts', سامانه مدیریت نشریات علمی دانشگاه اصفهان, 9(1), pp. 1-29. doi: 10.22108/jcs.2021.130065.1080
Khodayari Tehraninejad, Dina, Khoshnevis, Sedigheh. Proxima: Process Mining for Extracting Configurable Process Models Using Software Product Line Concepts. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 1400; 9(1): 1-29. doi: 10.22108/jcs.2021.130065.1080

Proxima: Process Mining for Extracting Configurable Process Models Using Software Product Line Concepts

Journal of Computing and Security
مقاله 2، دوره 9، شماره 1، فروردین 2022، صفحه 1-29    اصل مقاله (2.26 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2021.130065.1080
نویسندگان
Dina Khodayari Tehraninejad1؛ Sedigheh Khoshnevis* 2
1Department of Computer Engineering, Islamic Azad University, Malard Branch, Tehran, Iran.
2Department of Computer Engineering, Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran.
چکیده
In highly configurable information systems such as SaaS information systems, business process variability management is an important issue. The variability model, which is often called the configurable process model (CPM), can be reused to configure a family of processes each serving a separate purpose or customer. If not already present, these business process variability models have to be “extracted” based on event logs residing in the databases of the target enterprise(s). Such extraction is costly to carry out manually. In this study, inspired by Software Product Line Engineering concepts, we propose a novel automated process-mining-based method by extending the “Alpha” algorithm for process discovery as a preliminary solution. The proposed method takes a set of event logs as input; and in three phases, outputs a CPM in terms of a model called “BPFM”. To evaluate the method, we used the Goal-Question-Metric approach in a case study on 10 cases. For this purpose, input event logs were artificially extracted from the cases’ existing BPFM models and were fed as input to the proposed method. Then, we observed if the output models of the method were similar to the preliminary existing ones. The results showed that the method was promising in identifying the CPMs; since the extracted models involved activities that were 97.5% identical to what was expected. Moreover, a structural precision of 98% and a structural recall of 97.3% were obtained. The set of configurations derivable from the output models was 100% similar to and provided 100% coverage over the expected configurations.
کلیدواژه‌ها
Software Product Line؛ Variability Modeling؛ Process Mining؛ Configurable Process Model؛ Event Log
سایر فایل های مرتبط با مقاله
مراجع
[1] R. Sikal, H. Sbai, and L. Kjiri. Configurable Process Mining: Variability Discovery Approach. In 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pages 137--142. IEEE, 2018. [ bib | DOI ]
[2] H. Gomaa. Designing Software Product Lines with UML 2.0: From Use Cases to Pattern-Based Software Architectures. In 10th International Software Product Line Conference. IEEE Computer Society, 2006. [ bib | DOI ]
[3] K. Pohl, G. Böckle, and F. Van Der Linden. Software Product Line Engineering. Springer, 2005. [ bib ]
[4] A. Hallerbach, T. Bauer, and M. Reichert. Capturing variability in business process models: the Provop approach. Journal of Software Maintenance and Evolution: Research and Practice, 22(6-7):519--546, 2010. [ bib | DOI ]
[5] M. Moon, M. Hong, and K. Yeom. Two-Level Variability Analysis for Business Process with Reusability and Extensibility. In 2008 32nd Annual IEEE International Computer Software and Applications Conference, pages 263--270. IEEE, 2008. [ bib | DOI ]
[6] M. Razavian and R. Khosravi. Modeling Variability in Business Process Models Using UML. In Fifth International Conference on Information Technology: New Generations (itng 2008), pages 82--87. IEEE, 2008. [ bib | DOI ]
[7] W. Aalst, A. Adriansyah, A. Medeiros, F. Arcieri, T. Baier, T. Blickle, J. C. Bose, P. Brand, R. Brandtjen, J. Buijs, et al. Process Mining Manifesto. In International Conference on Business Process Management, page 169–194. Springer, 2011. [ bib | DOI ]
[8] R. Bashroush, M. Garba, R. Rabiser, I. Groher, and G. Botterweck. CASE Tool Support for Variability Management in Software Product Lines. ACM Computing Surveys (CSUR), 50(1):1–45, 2018. [ bib | DOI ]
[9] C. Rolland and S. Nurcan. Business Process Lines to Deal with the Variability. In 2010 43rd Hawaii International Conference on System Sciences, pages 1--10. IEEE, 2010. [ bib | DOI ]
[10] S. Khoshnevis and F. Shams. Automating identification of services and their variability for product lines using NSGA-II. Frontiers of Computer Science, 11(3):444--464, 2017. [ bib | DOI ]
[11] K. Yongsiriwit. Modeling and mining business process variants in cloud environments. Ph.D thesis, Université Paris-Saclay (ComUE), 2017. [ bib ]
[12] K. Suri, W. Gaaloul, and A. Cuccuru. Configurable IoT-Aware Allocation in Business Processes. In International Conference on Services Computing, pages 119--136. Springer, 2018. [ bib | DOI ]
[13] K. Suri. Modeling the internet of things in configurable process models. Ph.D thesis, Université Paris-Saclay, 2019. [ bib ]
[14] S. Saadatbakht and S. Khoshnevis. Extracting the Business Process Family Model from Business Processes in Software Product Lines. In 2018 4th International Conference on Optimization and Applications (ICOA), pages 1--6. IEEE, 2018. [ bib | DOI ]
[15] R. Sikal, H. Sbai, and L. Kjiri. Configurable process mining: A comparative study. In 19th National Iranian Computer Society Conference, Tehran, Iran, 2013. IEEE, 2013. [ bib | DOI ]
[16] N. van Beest, H. Groefsema, L. García-Bañuelos, and M. Aiello. Variability in business processes: Automatically obtaining a generic specification. Information Systems, 80:36--55, 2019. [ bib | DOI ]
[17] W. Van der Aalst, T. Weijters, and L. Maruster. Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128--1142, 2004. [ bib | DOI ]
[18] A. Khannat, H. Sbai, and L. Kjiri. Event Logs Pre-processing for Configurable Process Discovery: Ontology-Based Approach. In 2020 6th IEEE Congress on Information Science and Technology (CiSt), pages 139--144. IEEE, 2020. [ bib | DOI ]
[19] G. T. Lakshmanan and R. Khalaf. Leveraging Process-Mining Techniques. IT Professional, 15(5):22--30, 2012. [ bib | DOI ]
[20] H. H. Nguyen, La M. Rosa, M. Dumas-Menijvar, and A. ter Hofstede. Stage-based business process mining. In Proceedings of the Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017)(CEUR Workshop Proceedings, Volume 1848), pages 161--169. Sun SITE Central Europe, 2017. [ bib | DOI ]
[21] K. Yongsiriwit, M. Sellami, and W. Gaaloul. A Semantic Framework Supporting Business Process Variability Using Event Logs. In 2016 IEEE International Conference on Services Computing (SCC), pages 163--170. IEEE, 2016. [ bib | DOI ]
[22] A. Syamsiyah, A. Bolt, L. Cheng, B. F. Hompes, R. Jagadeesh Chandra Bose, B. F. v. Dongen, and W. M. van der Aalst. Business Process Comparison: A Methodology and Case Study. In International Conference on Business Information Systems, pages 253--267. Springer, 2017. [ bib | DOI ]
[23] Z. Yan, R. Dijkman, and P. Grefen. Fast Business Process Similarity Search with Feature-Based Similarity Estimation. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", pages 60--77. Springer, 2010. [ bib | DOI ]
[24] J. Park and K. Yeom. A Modeling Approach for Business Processes Based on Variability. In 2011 Ninth International Conference on Software Engineering Research, Management and Applications, pages 211--218. IEEE, 2011. [ bib | DOI ]
[25] S. P. Detro, E. A. P. Santos, H. Panetto, E. d. Freitas Rocha Loures, and M. Lezoche. Managing Business Process Variability Through Process Mining and Semantic Reasoning: An Application in Healthcare. In Working Conference on Virtual Enterprises, pages 333--340. Springer, 2017. [ bib | DOI ]
[26] A. Hmami, H. Sbai, and M. Fredj. Change Mining in Business Process Variability: A Comparative Study. In 2019 4th World Conference on Complex Systems (WCCS), pages 1--5. IEEE, 2019. [ bib | DOI ]
[27] P. Ardalani, C. Houy, P. Fettke, and P. Loos. Towards A Minimal Cost Of Change Approach For Inductive Reference Model Development. ECIS, 2013. [ bib | DOI ]
[28] M. Weidlich and M. Weske. Structural and behavioral commonalities of process variants. In ZEUS, pages 41--48, 2010. [ bib | DOI ]
[29] C. Li, M. Reichert, and A. Wombacher. Mining business process variants: Challenges, scenarios, algorithms. Data & Knowledge Engineering, 70(5):409--434, 2011. [ bib | DOI ]
[30] A. Martens, P. Fettke, and P. Loos. A Genetic Algorithm for the Inductive Derivation of Reference Models Using Minimal Graph-Edit Distance Applied to Real-World Business Process Data. Tagungsband Multikonferenz Wirtschaftsinformatik, pages 1613--1626, 2014. [ bib | DOI ]
[31] J. Rehse, P. Fettke, and P. Loos. A graph-theoretic method for the inductive development of reference process models. Software & Systems Modeling, 16(3):833–873, 2017. [ bib | DOI ]
[32] H. Scholta, M. Niemann, P. Delfmann, M. Räckers, and J. Becker. Semi-automatic inductive construction of reference process models that represent best practices in public administrations: A method. Information Systems, 84:63--87, 2019. [ bib | DOI ]
[33] N. Assy, W. Gaaloul, and B. Defude. Mining Configurable Process Fragments for Business Process Design. In International Conference on Design Science Research in Information Systems, pages 209--224. Springer, 2014. [ bib | DOI ]
[34] N. Assy, N. N. Chan, and W. Gaaloul. An Automated Approach for Assisting the Design of Configurable Process Models. IEEE Transactions on Services Computing, 8(6):874 -- 888, 2015. [ bib | DOI ]
[35] J. C. Buijs, B. F. van Dongen, and W. M. van der Aalst. Mining configurable process models from collections of event logs. In Business process management, pages 33--48. Springer, 2013. [ bib ]
[36] A. Mejri and S. A. Ghannouchi. Discovering Reference Process Models in the Context of BPM Projects. Procedia Technology, 9:489--497, 2013. [ bib | DOI ]
[37] F. Gottschalk, W. M. van der Aalst, and M. H. Jansen-Vullers. Mining Reference Process Models and Their Configurations. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", page 263–272. Springer, 2008. [ bib | DOI ]
[38] V. S. Dani, C. M. D. S. Freitas, and L. Thom. Ten years of visualization of business process models: A systematic literature review. Computer Standards & Interfaces, 66:103347, 2019. [ bib | DOI ]
[39] M. Song, C. W. Günther, and W. M. Van der Aalst. Trace Clustering in Process Mining. In International Conference on Business Process Management, page 109–120. Springer, 2008. [ bib | DOI ]
[40] H. Cheng and A. Kumar. Process mining on noisy logs — Can log sanitization help to improve performance? Decision Support Systems, 79:138--149, 2015. [ bib | DOI ]
[41] R. Van Solingen, V. Basili, G. Caldiera, and H. D. Rombach. Goal Question Metric (GQM) Approach. Encyclopedia of Software Engineering, 2002. [ bib | DOI ]
[42] A. A. De Medeiros, W. M. van der Aalst, and A. Weijters. Quantifying process equivalence based on observed behavior. Data & Knowledge Engineering, 64(1):55--74, 2008. [ bib | DOI ]
[43] P. Runeson and M. Höst. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering, 14(2):131--164, 2009. [ bib | DOI ]
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