Process Mining Tools Comparison

Yıl 2018, Cilt: 9 Sayı: 34, 97 - 104, 01.09.2018

Ufuk Çelik Eyüp Akçetin

https://doi.org/10.5824/1309-1581.2018.4.007.x

Öz

Process mining is a new era in the science of data mining and is a subset of business intelligence. Process mining analysis provides an idea about a general process by comparing each process with others in the terms of time and responsible people who deal with the process. For this reason, event logs are checked. Event logs consist of large data. Because the event logs keep all the records that occur during short time intervals. Special programs are needed to examine such data. These programs generate a process map using information such as event ID, activity, time and responsible person. Through the analysis, processes are discovered, monitored and improved. In this study, the tools named ProM, Disco, Celonis and My-Invenio used in process mining were examined and their performance according to usage features compared. According to the obtained results, the usefulness, performance and reporting features of the software used in a process analysis are revealed.

Anahtar Kelimeler

Data Mining, Business Intelligence, Process Mining

Süreç Madenciliği Araçları Karşılaştırması

Öz

Süreç madenciliği veri madenciliği biliminde yeni bir alan olup iş zekasının bir alt koludur. Süreç madenciliği analizi her bir sürecin kim tarafından ve hangi zaman aralığında yapıldığını diğer süreçlerle kıyaslayarak genel sürecin işleyişi hakkında fikir edinmeye çalışır. Bu sebeple olay günlüklerine bakılır. Olay günlükleri ise büyük verilerden oluşur. Çünkü olay günlükleri kısa zaman aralıklarında meydana gelen tüm kayıtları saklar. Bu tür verilerin incelenmesi için özel programlara ihtiyaç vardır. Bu programlar olay kimliği, aktivite, zaman ve sorumlu kişiler gibi bilgileri kullanarak bir süreç haritası ortaya çıkartırlar. Bu analiz sayesinde süreçler keşfedilir, izlenir ve geliştirilir. Bu çalışmada süreç madenciliğinde kullanılan ProM, Disco, Celonis ve My-Invenio isimli araçlar incelenmiş ve kullanım özelliklerine göre performansları kıyaslanmıştır. Elde edilen sonuçlar sayesinde bir sürecin analizinde kullanılan yazılımların etkin kullanım, performans ve raporlama özellikleri ortaya konmuştur.

Anahtar Kelimeler

Veri Madenciliği, İş Zekası, Süreç Madenciliği

Kaynakça

• Bernardi, M. L., Cimitile, M., Di Francescomarino, C., & Maggi, F. M. (2014). Using Discriminative Rule Mining to Discover Declarative Process Models with Non-atomic Activities BT - Rules on the Web. From Theory to Applications. In A. Bikakis, P. Fodor, & D. Roman (Eds.) (pp. 281–295). Cham: Springer International Publishing.
• Celonis. (2017). Celonis Process Mining. Retrieved October 1, 2017, from http://www.celonis.com/en/
• Der, Van Aalst, W. M. P., de Leoni, M., & ter Hofstede, A. H. M. (2011). Process mining and visual analytics: Breathing life into business process models. BPM Center Report BPM-11-15, BPMcenter. Org, 17, 699–730.
• Devi, A. T. (2006). An informative and comparative study of process mining tools. Int. J. Sci. Eng. Res, 8(5), 8–10.
• Fluxicon. (2017). Disco. Retrieved October 1, 2017, from https://fluxicon.com/disco/
• Günther, C. W. (2009). Process Mining in Flexible Environments. Technische Universiteit Eindhoven.
• Technische Universiteit Eindhoven, Eindhoven University of Technology Library.
• Helm, E., & Paster, F. (2015). First Steps Towards Process Mining in Distributed Health Information Systems. International Journal of Electronics and Telecommunications. https://doi.org/10.1515/eletel-2015-0017
• Kebede, M. (2015). Comparative Evaluation of Process Mining Tools. University of Tartu.
• Kumaraguru, P. V, & Rajagopalan, D. S. P. (2013). Machine learning approach for model discovery and process enhancement using process mining techniques. Department of Computer Science.
• Dr. M.G.R. Educational and Research Institute, India.
• My-Invenio. (2017). Retrieved October 1, 2017, from https://www.my-invenio.com Petri, C. A. (1962). Kommunikation mit Automaten. Hamburg.
• Petri, C. A., & Reisig, W. (2008). Petri net. Scholarpedia, 3, 6477.
• Process Mining. (2017). Retrieved October 1, 2017, from http://www.processmining.org ProM 6. (2017). Retrieved October 1, 2017, from http://www.promtools.org/
• Rinke, A. (2017). Why Manufacturers Need Process Mining — A New Type Of Big Data Analytics.
• Rozinat, A., & Günther, C. W. (2012). Disco. Retrieved from https://fluxicon.com/disco/
• Rozinat, A., & Van Der Aalst, W. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33(1), 64–95. https://doi.org/10.1016/j.is.2007.07.001
• Rozinat, A., & van der Aalst, W. M. P. (2006). Decision Mining in ProM BT - Business Process
• Management. In S. Dustdar, J. L. Fiadeiro, & A. P. Sheth (Eds.) (pp. 420–425). Berlin, Heidelberg: Springer Berlin Heidelberg.
• Song, M., Günther, C. W., & van der Aalst, W. M. P. (2009). Trace Clustering in Process Mining BT - Business Process Management Workshops. In D. Ardagna, M. Mecella, & J. Yang (Eds.) (pp 109–120). Berlin, Heidelberg: Springer Berlin Heidelberg.
• Van Der Aalst, W. (2011). Process mining: discovery, conformance and enhancement of business processes. Springer Science & Business Media.
• Van Der Aalst, W., Adriansyah, A., de Medeiros, A. K. A., Arcieri, F., Baier, T., Blickle, T., … Wynn, M. (2012). Process Mining Manifesto. In F. Daniel, K. Barkaoui, & S. Dustdar (Eds.), Business Process Management Workshops: BPM 2011 International Workshops, Clermont-Ferrand, France, August 29, 2011, Revised Selected Papers, Part I (pp. 169–194). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-28108-2_19
• Van der Aalst, W., Adriansyah, A., & van Dongen, B. (2012). Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(2), 182–192.
• Van Der Aalst, W., & Damiani, E. (2015). Processes Meet Big Data: Connecting Data Science with Process Science. IEEE Transactions on Services Computing, 8(6), 810–819. https://doi.org/10.1109/TSC.2015.2493732
• Van Der Aalst, W., de Beer, H. T., & van Dongen, B. F. (2005). Process Mining and Verification of Properties: An Approach Based on Temporal Logic. In R. Meersman & Z. Tari (Eds.), On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2005, Agia Napa, Cyprus, October 31 - November 4, 2005, Proceedings, Part I (pp. 130–147). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11575771_11
• Van Der Aalst, W., de Medeiros, A. K. A., & Weijters, A. J. M. M. (2005). Genetic Process Mining. In G. Ciardo & P. Darondeau (Eds.), Applications and Theory of Petri Nets 2005: 26th International Conference, ICATPN 2005, Miami, USA, June 20-25, 2005. Proceedings (pp. 48–69). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11494744_5
• Van Der Aalst, W., Dongen, B. van, Herbst, J., Maruster, L., Schimm, G., & Weijters, A. J. M. M. (2003).
• Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering, 47(2), 237–267.
• van der Aalst, W. M. P. (2005). Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Engineering, 10, 198–211.
• Van Der Aalst, W., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713–732.https://doi.org/http://dx.doi.org/10.1016/j.is.2006.05.003