Comparing Concept Drift Detection with Process Mining Software
DOI:
https://doi.org/10.5753/isys.2020.832Abstract
Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use.
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Referências
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Braha, D. (2013). Data mining for design and manufacturing: methods and applications, volume 3. Springer Science & Business Media. iSys: Revista Brasileira de Sistemas de Informação (iSys: Brazilian Journal of Information Systems) http://seer.unirio.br/index.php/isys/
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Harremoes, P. and Tusnady, G. (2012). Information divergence is more χ2-distributed than the χ2-statistics. In 2012 IEEE International Symposium on Information Theory Proceedings, pages 533–537.
Hompes, B., Buijs, J., Van der Aalst, W., Dixit, P., and Buurman, J. (2015). Discovering deviating cases and process variants using trace clustering. In Proceedings of the 27th Benelux Conference on Artificial Intelligence (BNAIC), November, pages 5–6.
Jans, M., van der Werf, J. M., Lybaert, N., and Vanhoof, K. (2011). A business process mining application for internal transaction fraud mitigation. Expert Systems with Applications, 38(10):13351 – 13359.
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., and Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion, 37:132 –156.
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Maaradji, A., Dumas, M., Rosa, M., and Ostovar, A. (2015). Fast and accurate business process drift detection. In Proceedings of the 13th International Conference on Business Process Management - Volume 9253, pages 406–422, Berlin, Heidelberg. Springer-Verlag.
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Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A. H. M., and van Dongen, B. F. V. (2016). Detecting drift from event streams of unpredictable business processes.
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Rojas, E., Munoz-Gama, J., Sepulveda, M., and Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61:224 – 236.
Rosa, M. L., Reijers, H. A., van der Aalst, W. M., Dijkman, R. M., Mendling, J., Dumas, M., and Garcıa-Banuelos, L. (2011). Apromore: An advanced process model repository. Expert Systems with Applications, 38(6):7029 – 7040.
Rozinat, A. and van der Aalst, W. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33(1):64 – 95.
Seeliger, A., Nolle, T., and Muhlhauser, M. (2017). Detecting concept drift in processes using graph metrics on process graphs. In Proceedings of the 9th Conference on Subject-oriented Business Process Management, S-BPM ONE ’17, pages 6:1–6:10, New York, NY, USA. ACM.
Tavares, G. M., Ceravolo, P., Turrisi Da Costa, V. G., Damiani, E., and Barbon Junior, S.(2019). Overlapping analytic stages in online process mining. In 2019 IEEE International Conference on Services Computing (SCC), pages 167–175.
Tavares, G. M., da Costa, V. G. T., Ceravolo, P., Martins, V. E., and Barbon Jr, S. (2018). Anomaly detection in busi-ness process based on data stream mining. In In Proceedings of XIV Brazilian Symposium on Information Systems (SBSI’18), Caxias do Sul, RS, Brazil. SBC.
van der Aalst, W., Adriansyah, A., and van Dongen, B. (2011a). Causal nets: A modeling language tailored towards process discovery. In Katoen, J.-P. and Konig, B., editors, CONCUR 2011 – Concurrency Theory, pages 28–42, Berlin, Heidelberg. Springer Berlin Heidelberg.
van der Aalst, W., Schonenberg, M., and Song, M. (2011b). Time prediction based on process mining. Information Systems, 36(2):450 – 475. Special Issue: Semantic Integration of Data, Multimedia, and Services.
van der Aalst, W., Weijters, T., and Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128–1142.
van der Aalst, W. e. a. (2012). Process mining manifesto. In Business Process Management Workshops, pages 169–194, Berlin, Heidelberg. Springer Berlin Heidelberg.
van der Aalst, W. M. P. (2004). Business Process Management Demystified: A Tutorial on Models, Systems and Standards for Workflow Management, pages 1–65. Springer Berlin Heidelberg, Berlin, Heidelberg.
van der Aalst, W. M. P. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Publishing Company, Incorporated, 1st edition.
van Dongen, B. F., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A. J. M. M., and van der Aalst, W. M. P. (2005). The prom framework: A new era in process mining tool support. In Ciardo, G. and Darondeau, P., editors, Applications and Theory of Petri Nets 2005, pages 444–454, Berlin, Heidelberg. Springer Berlin Heidelberg.
Weijters, A. J. M. M. and van der Aalst, W. M. P. (2003). Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng., 10(2):151–162.
Zheng, C., Wen, L., and Wang, J. (2017). Detecting process concept drifts from event logs. In Panetto, H., Debruyne, C., Gaaloul, W., Papazoglou, M., Paschke, A., Ardagna, C. A., and Meersman, R., editors, On the Move to Meaningful Internet Systems. OTM 2017 Conferences, pages 524–542, Cham. Springer International Publishing.
Accorsi, R. and Stocker, T. (2012). Discovering workflow changes with time-based trace clustering. In Aberer, K., Damiani, E., and Dillon, T., editors, Data-Driven Process Discovery and Analysis, pages 154–168, Berlin, Heidelberg. Springer Berlin Heidelberg.
Alves De Medeiros, A., Dongen, van, B., Aalst, van der, W., and Weijters, A. (2004). Process mining : extending the alpha-algorithm to mine short loops. BETA publicatie: working papers. Technische Universiteit Eindhoven.
Barbon Junior, S., Tavares, G. M., da Costa, V. G. T., Ceravolo, P., and Damiani, E.(2018). A framework for human-in-the-loop monitoring of concept-drift detection in event log stream. In Companion Proceedings of the The Web Conference 2018, WWW ’18, pages 319–326, Republic and Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee.
Bose, R. J. C. (2012). Process mining in the large: preprocessing, discovery, and diagnostics. PhD thesis, Eindhoven University of Technology, Eindhoven. Bose, R. P. J. C., van der Aalst, W. M. P.,Zliobaite, I., and Pechenizkiy, M. (2014). Dealing with concept drifts in process mining. IEEE Transactions on Neural Networks and Learning Systems, 25(1):154–171.
Braha, D. (2013). Data mining for design and manufacturing: methods and applications, volume 3. Springer Science & Business Media. iSys: Revista Brasileira de Sistemas de Informação (iSys: Brazilian Journal of Information Systems) http://seer.unirio.br/index.php/isys/
Buijs, J. C. A. M., van Dongen, B. F., and van der Aalst, W. M. P. (2012). On the role of fitness, precision, generalization and simplicity in process discovery. In Meersman, R., Panetto, H., Dillon, T., Rinderle-Ma, S., adam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., and Cruz, I. F., editors, On the Move to Meaningful Internet Systems: OTM 2012, pages 305–322, Berlin, Heidelberg. Springer Berlin Heidelberg.
Cao, F., Estert, M., Qian, W., and Zhou, A. (2006). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining, pages 328–339. SIAM.
Gama, J., Rodrigues, P. P., Spinosa, E., and Carvalho, A. (2010). Knowledge Discovery from Data Streams. Web Intelligence and Security - Advances in Data and Text Mining Techniques for Detecting and Preventing Terrorist Activities on the Web, pages 125–138.
Harremoes, P. and Tusnady, G. (2012). Information divergence is more χ2-distributed than the χ2-statistics. In 2012 IEEE International Symposium on Information Theory Proceedings, pages 533–537.
Hompes, B., Buijs, J., Van der Aalst, W., Dixit, P., and Buurman, J. (2015). Discovering deviating cases and process variants using trace clustering. In Proceedings of the 27th Benelux Conference on Artificial Intelligence (BNAIC), November, pages 5–6.
Jans, M., van der Werf, J. M., Lybaert, N., and Vanhoof, K. (2011). A business process mining application for internal transaction fraud mitigation. Expert Systems with Applications, 38(10):13351 – 13359.
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., and Wozniak, M. (2017). Ensemble learning for data stream analysis: A survey. Information Fusion, 37:132 –156.
Leemans, S. J. J., Fahland, D., and van der Aalst, W. M. P. (2014). Process and Deviation Exploration with Inductive Visual Miner, volume 1295 of CEUR Workshop Proceedings, pages 46–50. CEUR-WS.org.
Liu, N., Huang, J., and Cui, L. (2018). A framework for online process concept drift detection from event streams. In 2018 IEEE International Conference on Services Computing (SCC), pages 105–112.
Maaradji, A., Dumas, M., Rosa, M., and Ostovar, A. (2015). Fast and accurate business process drift detection. In Proceedings of the 13th International Conference on Business Process Management - Volume 9253, pages 406–422, Berlin, Heidelberg. Springer-Verlag.
Omori, N. J., Tavares, G. M., Ceravolo, P., and Barbon, Jr., S. (2019). Comparing concept drift detection with process mining tools. In Proceedings of the XV Brazilian Symposium on Information Systems, SBSI’19, pages 31:1–31:8, New York, NY, USA. ACM.
Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A. H. M., and van Dongen, B. F. V. (2016). Detecting drift from event streams of unpredictable business processes.
In Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., and Saeki, M., editors, Conceptual Modeling, pages 330–346, Cham. Springer International Publishing.
Rojas, E., Munoz-Gama, J., Sepulveda, M., and Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61:224 – 236.
Rosa, M. L., Reijers, H. A., van der Aalst, W. M., Dijkman, R. M., Mendling, J., Dumas, M., and Garcıa-Banuelos, L. (2011). Apromore: An advanced process model repository. Expert Systems with Applications, 38(6):7029 – 7040.
Rozinat, A. and van der Aalst, W. (2008). Conformance checking of processes based on monitoring real behavior. Information Systems, 33(1):64 – 95.
Seeliger, A., Nolle, T., and Muhlhauser, M. (2017). Detecting concept drift in processes using graph metrics on process graphs. In Proceedings of the 9th Conference on Subject-oriented Business Process Management, S-BPM ONE ’17, pages 6:1–6:10, New York, NY, USA. ACM.
Tavares, G. M., Ceravolo, P., Turrisi Da Costa, V. G., Damiani, E., and Barbon Junior, S.(2019). Overlapping analytic stages in online process mining. In 2019 IEEE International Conference on Services Computing (SCC), pages 167–175.
Tavares, G. M., da Costa, V. G. T., Ceravolo, P., Martins, V. E., and Barbon Jr, S. (2018). Anomaly detection in busi-ness process based on data stream mining. In In Proceedings of XIV Brazilian Symposium on Information Systems (SBSI’18), Caxias do Sul, RS, Brazil. SBC.
van der Aalst, W., Adriansyah, A., and van Dongen, B. (2011a). Causal nets: A modeling language tailored towards process discovery. In Katoen, J.-P. and Konig, B., editors, CONCUR 2011 – Concurrency Theory, pages 28–42, Berlin, Heidelberg. Springer Berlin Heidelberg.
van der Aalst, W., Schonenberg, M., and Song, M. (2011b). Time prediction based on process mining. Information Systems, 36(2):450 – 475. Special Issue: Semantic Integration of Data, Multimedia, and Services.
van der Aalst, W., Weijters, T., and Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9):1128–1142.
van der Aalst, W. e. a. (2012). Process mining manifesto. In Business Process Management Workshops, pages 169–194, Berlin, Heidelberg. Springer Berlin Heidelberg.
van der Aalst, W. M. P. (2004). Business Process Management Demystified: A Tutorial on Models, Systems and Standards for Workflow Management, pages 1–65. Springer Berlin Heidelberg, Berlin, Heidelberg.
van der Aalst, W. M. P. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Publishing Company, Incorporated, 1st edition.
van Dongen, B. F., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A. J. M. M., and van der Aalst, W. M. P. (2005). The prom framework: A new era in process mining tool support. In Ciardo, G. and Darondeau, P., editors, Applications and Theory of Petri Nets 2005, pages 444–454, Berlin, Heidelberg. Springer Berlin Heidelberg.
Weijters, A. J. M. M. and van der Aalst, W. M. P. (2003). Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng., 10(2):151–162.
Zheng, C., Wen, L., and Wang, J. (2017). Detecting process concept drifts from event logs. In Panetto, H., Debruyne, C., Gaaloul, W., Papazoglou, M., Paschke, A., Ardagna, C. A., and Meersman, R., editors, On the Move to Meaningful Internet Systems. OTM 2017 Conferences, pages 524–542, Cham. Springer International Publishing.
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2020-07-31
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Omori, N. J., Tavares, G. M., Ceravolo, P., & Barbon Jr, S. (2020). Comparing Concept Drift Detection with Process Mining Software. ISys - Revista Brasileira De Sistemas De Informação, 13(4), 101–125. https://doi.org/10.5753/isys.2020.832
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