Abstract
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.
- Rafael Accorsi and Thomas Stocker. 2012. Discovering workflow changes with time-based trace clustering. In Proceedings of the Data-Driven Process Discovery and Analysis. Springer, Berlin, 154–168.Google ScholarCross Ref
- Charu C. Aggarwal and Chandan K. Reddy (Eds.). 2014. Data Clustering Algorithms and Apllications. CRC Press, Boca Raton, FL.Google Scholar
- Mihael Ankerst, Markus M. Breunig, Hans Peter Kriegel, and Jörg Sander. 1999. OPTICS: Ordering points to identify the clustering structure. SIGMOD Record (ACM Special Interest Group on Management of Data) 28, 2 (Jun. 1999), 49–60.Google ScholarDigital Library
- Sylvio Barbon Junior, Gabriel Marques Tavares, Victor G. Turrisi da Costa, Paolo Ceravolo, and Ernesto Damiani. 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. Vol. 2, ACM, 319–326.Google Scholar
- A. Batyuk, V. V. Oityshyn, and V. Verhun. 2018. Software architecture design of the real- time processes monitoring platform. In Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining Processing. IEEE, 98–101.Google Scholar
- Anatoliy Batyuk and Volodymyr Voityshyn. 2020. Streaming process discovery method for semi-structured business processes. In Proceedings of the 2020 IEEE 3rd International Conference on Data Stream Mining and Processing. IEEE, 444–448.Google ScholarCross Ref
- Albert Bifet and Ricard Gavaldà. 2007. Learning from time-changing data with adaptive windowing. In Proceedings of the 7th SIAM International Conference on Data Mining. Springer, 443–448.Google ScholarCross Ref
- R. P. Jagadeesh Chandra Bose and Wil M. P. P van der Aalst. 2010. Trace clustering based on conserved patterns: Towards achieving better process models. In Lecture Notes in Business Information Processing. S. Rinderle-Ma, S. Sadiq, and F. Leymann (Eds.), Vol. 43, Springer, Berlin, 170–181.Google Scholar
- R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indrė Žliobaitė, and Mykola Pechenizkiy. 2011. Handling concept drift in process mining. In Advanced Information Systems Engineering, Haralambos Mouratidis and Colette Rolland (Eds.), Springer, Berlin, 391–405.Google Scholar
- R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst, Indre Zliobaite, and Mykola Pechenizkiy. 2014. Dealing with concept drifts in process mining. IEEE Transactions on Neural Networks and Learning Systems 25, 1 (Jan. 2014), 154–171.Google Scholar
- Markus M. Breuniq, Hans Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: Identifying density-based local outliers. SIGMOD Record (ACM Special Interest Group on Management of Data) 29, 2 (Jun. 2000), 93–104.Google Scholar
- Tobias Brockhoff, Merih Seran Uysal, and Wil M. P. van der Aalst. 2020. Time-aware concept drift detection using the earth mover’s distance. In Proceedings of the 2nd International Conference on Process Mining. IEEE, 33–40.Google Scholar
- Andrea Burattin, Marta Cimitile, and Fabrizio Maria Maggi. 2015. Lights, camera, action! Business process movies for online process discovery. In Business Process Management Workshops. Fabiana Fournier, and Jan Mendling (Eds.), Springer, 408–419.Google ScholarCross Ref
- A. Burattin, M. Cimitile, F. M. Maggi, and A. Sperduti. 2015. Online discovery of declarative process models from event streams. IEEE Transactions on Services Computing 8, 6 (2015), 833–846.Google ScholarCross Ref
- A. Burattin, A. Sperduti, and W. M. P. van der Aalst. 2014. Control-flow discovery from event streams. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation. IEEE, 2420–2427.Google Scholar
- Giorgio C. Buttazzo. 2011. Hard Real-Time Computing Systems (3rd ed.). Real-Time Systems Series, Vol. 24. Springer, New York, NY. 1689–1699Google Scholar
- Feng Cao, Martin Estert, Weining Qian, and Aoying Zhou. 2006. Density-based clustering over an evolving data stream with noise. In Proceedings of the 6th SIAM International Conference on Data Mining. SIAM, 328–339.Google ScholarCross Ref
- Josep Carmona and Ricard Gavald. 2012. Online techniques for dealing with concept drift in process mining. In Proceedings of the 11th International Conference on Advances in Intelligent Data Analysis. Springer, Berlin, 90–102.Google ScholarDigital Library
- Gabriel Marques Tavares, Sylvio Barbon Jr., and Paolo Ceravolo. 2019. Synthetic Event Streams. IEEE Dataport. DOI:https://dx.doi.org/10.21227/2kxd-m509Google Scholar
- Paolo Ceravolo, Gabriel Marques Tavares, Sylvio Barbon Junior, and Ernesto Damiani. 2020. Evaluation goals for online process mining: A concept drift perspective. IEEE Transactions on Services Computing (2020), 1–1 . https://ieeexplore.ieee.org/document/9124702.Google Scholar
- R. Conforti, M. L. Rosa, and A. H. M. t. Hofstede. 2017. Filtering out infrequent behavior from business process event logs. IEEE Transactions on Knowledge and Data Engineering 29, 2 (2017), 300–314.Google ScholarDigital Library
- A. K. A. De Medeiros, B. F. Van Dongen, W. M. P. Van Der Aalst, and A. J. M. M. Weijters. 2004. Process mining for ubiquitous mobile systems: An overview and a concrete algorithm. Proceedings of the 2nd CAiSE Conference on Ubiquitous Mobile Information and Collaboration Systems. Vol. 3272, 151–165.Google Scholar
- Gregory Ditzler, Manuel Roveri, Cesare Alippi, and Robi Polikar. 2015. Learning in nonstationary environments: A survey. IEEE Computational Intelligence Magazine 10, 4 (Nov. 2015), 12–25.Google ScholarDigital Library
- Maikel Eck, Xixi Lu, Sander Leemans, and Wil Aalst. 2015. PM2: A process mining project methodology. In Advanced Information Systems Engineering. J. Zdravkovic, M. Kirikova, and P. Johannesson (Eds.), Springer, 297–313.Google Scholar
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. AAAI Press, 226–231.Google Scholar
- Bin Fan, Dave G. Andersen, Michael Kaminsky, and Michael D. Mitzenmacher. 2014. Cuckoo filter: Practically better than bloom. In Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies. Association for Computing Machinery, New York, NY, 75–88.Google Scholar
- Eibe Frank and Ian H. Witten. 1998. Using a permutation test for attribute selection in decision trees. In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA, 152–160.Google Scholar
- João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM Computing Surveys 46, 4 (2014), 1–37.Google ScholarDigital Library
- Cleiton dos Santos Garcia, Alex Meincheim, Elio Ribeiro Faria Junior, Marcelo Rosano Dallagassa, Denise Maria Vecino Sato, Deborah Ribeiro Carvalho, Eduardo Alves Portela Santos, and Edson Emilio Scalabrin. 2019. Process mining techniques and applications—A systematic mapping study. Expert Systems with Applications 133 (Nov. 2019), 260–295.https://www.sciencedirect.com/science/article/abs/pii/S0957417419303161.Google Scholar
- Christian W. Günther and Wil M. P. Van Der Aalst. 2007. Fuzzy mining—Adaptive process simplification based on multi-perspective metrics. In International Conference on Business Process Management. G. Alonso, P. Dadam, and M. Rosemann (Eds.), Vol. 4714, Springer, Berlin, 328–343.Google Scholar
- Marwan Hassani. 2019. Concept drift detection of event streams using an adaptive window. In Proceedings of the 33rd International European Council for Modelling and Simulation Vol. 33. European Council for Modelling and Simulation, 230–239.Google ScholarCross Ref
- Bart Hompes, Wil M. P. van der Aalst, and Prabhakar Dixit. 2017. Detecting changes in process behavior using comparative case clustering. In Proceedings of the International Symposium Data-Driven Process Discovery and Analysis. Vol. 244. Springer, 0–22.Google Scholar
- Angelo Impedovo, Paolo Mignone, Corrado Loglisci, and Michelangelo Ceci. 2020. Simultaneous process drift detection and characterization with pattern-based change detectors. In International Conference on Discovery Science A. Appice, G. Tsoumakas, Y. Manolopoulos, and S. Matwin (Eds.). Vol. 12323, Springer, 451–467. https://link.springer.com/chapter/10.1007%2F978-3-030-61527-7_30#citeas.Google ScholarDigital Library
- R. Killick, P. Fearnhead, and I. A. Eckley. 2012. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 107, 500 (Jan. 2012), 1590–1598.Google ScholarCross Ref
- B. Kitchenham and S. Charters. 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report 3. Keele University and Durham University Joint Report.Google Scholar
- Angelina Prima Kurniati, Ciarán McInerney, Kieran Zucker, Geoff Hall, David Hogg, and Owen Johnson. 2020. Using a multi-level process comparison for process change analysis in cancer pathways. International Journal of Environmental Research and Public Health 17, 19 (2020), 1–16.Google ScholarCross Ref
- Johnson O. Kurniati A. P., McInerney C., Zucker K., Hall G., Hogg D.2019. A multi-level approach for identifying process change in cancer pathways. In International Conference on Business Process Management Workshops. Springer, 595–607.Google Scholar
- Marcello La Rosa, Hajo A. Reijers, Wil M. P. Van Der Aalst, Remco M. Dijkman, Jan Mendling, Marlon Dumas, and Luciano García-Bañuelos. 2011. APROMORE: An advanced process model repository. Expert Systems with Applications 38, 6 (Jun. 2011), 7029–7040.Google Scholar
- Sander J. J. Leemans, Dirk Fahland, and Wil M. P. Van Der Aalst. 2013. Discovering block-structured process models from event logs—A constructive approach. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). J. M. Colom and J. Desel (Eds.), Vol. 7927, Springer, Berlin, 311–329.Google Scholar
- Leilei Lin, Lijie Wen, Li Lin, Jisheng Pei, and Hedong Yang. 2020. LCDD: Detecting business process drifts based on local completeness. IEEE Transactions on Services Computing 14, 8 (2020), 1–1.Google Scholar
- N. Liu, J. Huang, and L. Cui. 2018. A framework for online process concept drift detection from event streams. In Proceedings of the 2018 IEEE International Conference on Services Computing. IEEE, 105–112.Google Scholar
- Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang. 2019. Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering 31, 12 (Dec. 2019), 2346–2363.Google Scholar
- Yang Lu. 2017. Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration 6 (Jun. 2017), 1–10. https://www.sciencedirect.com/science/article/abs/pii/S2452414X17300043.Google ScholarCross Ref
- Daniela Luengo and Marcos Sepúlveda. 2012. Applying clustering in process mining to find different versions of a business process that changes over time. In Business Process Management Workshops. Florian Daniel, Kamel Barkaoui, and Schahram Dustdar (Eds.), Springer, Berlin, 153–158.Google ScholarCross Ref
- Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, and Alireza Ostovar. 2015. Fast and accurate business process drift detection. In International Conference on Business Process Management. H. Motahari-Nezhad, J. Recker, M. Weidlich (Eds.), Springer, 406–422.Google ScholarDigital Library
- A. Maaradji, M. Dumas, M. L. Rosa, and A. Ostovar. 2017. Detecting sudden and gradual drifts in business processes from execution traces. IEEE Transactions on Knowledge and Data Engineering 29, 10 (2017), 2140–2154.Google ScholarDigital Library
- Fabrizio Maria Maggi, Andrea Burattin, Marta Cimitile, and Alessandro Sperduti. 2013. Online process discovery to detect concept drifts in LTL-based declarative process models. In On the Move to Meaningful Internet Systems (OTM’13), R. Meersman et al. (Eds). Lecture Notes in Computer Science, vol 8185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41030-7_7Google Scholar
- Gurmeet Singh Manku and Rajeev Motwani. 2002. Approximate frequency counts over data streams. In 28th International Conference on Very Large Databases. Philip A. Bernstein, Yannis E. Ioannidis, Raghu Ramakrishnan, and Dimitris Papadias (Eds.) Morgan Kaufmann, San Francisco, CA, 346–357.Google ScholarCross Ref
- M. V. Manoj Kumar, Likewin Thomas, and B. Annappa. 2015. Capturing the sudden concept drift in process mining. In Proceedings of the CEUR Workshop. CEUR-WS.org, Brussels, 132–143.Google Scholar
- Giovanni Da San Martino, Nicolò Navarin, and Alessandro Sperduti. 2013. A lossy counting based approach for learning on streams of graphs on a budget. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. AAAI Press, 1294–1301.Google Scholar
- J. Martjushev, R. P. Jagadeesh Chandra Bose, and Wil M. P. Van Der Aalst. 2015. Change point detection and dealing with gradual and multi-order dynamics in process mining. In Proceedings of the International Conference on Business Informatics Research. Springer, 1–15.Google Scholar
- Martial Mermillod, Aurélia Bugaiska, and Patrick Bonin. 2013. The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects. Frontiers in Psychology 4 (Aug. 2013), 504.Google Scholar
- Davide Mora, Paolo Ceravolo, Ernesto Damiani, and Gabriel Marques Tavares. 2020. The CDESF toolkit: An introduction. In ICPM Doctoral Consortium and Tool Demonstration Track 2020, Vol. 2703. CEUR-WS.org, Padua, 47–50.Google Scholar
- Nicolas Jashchenko Omori, Gabriel Marques Tavares, Paolo Ceravolo, and Sylvio Barbon. 2019. Comparing concept drift detection with process mining tools. In Proceedings of the 15th Brazilian Symposium on Information Systems. ACM, 1–8.Google ScholarDigital Library
- Alireza Ostovar, Sander J. J. Leemans, and Marcello La Rosa. 2020. Robust drift characterization from event streams of business processes. ACM Transactions on Knowledge Discovery from Data 14, 3, Article 30 (March 2020), 57 pages.Google Scholar
- Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, and Arthur Ter. 2017. Characterizing drift from event streams of business processes. In Lecture Notes in Computer Science. E. Dubois and K. Pohl (Eds.), Vol. 10253, Springer, 210–228.Google Scholar
- Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, Arthur H. M. ter Hofstede, and Boudewijn F. V. van Dongen. 2016. Detecting drift from event streams of unpredictable business processes. In Proceedings of the International Conference on Conceptual Modeling ER 2016 (Lecture Notes in Computer Science). Springer, Cham, 330–346.Google Scholar
- Rohit J. Parikh. 1966. On context-free languages. Journal of the Association for Computing Machinery 13, 4 (1966), 570–581.Google ScholarDigital Library
- Stephen Pauwels and Toon Calders. 2019. An anomaly detection technique for business processes based on extended dynamic Bayesian networks. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing.494–501.Google ScholarDigital Library
- David Redlich, Thomas Molka, Wasif Gilani, Gordon Blair, and Awais Rashid. 2014. Scalable dynamic business process discovery with the constructs competition miner. In Proceedings of the 4th International Symposium on Data-Driven Process Discovery and Analysis. Vol. 1293. CEUR-WS.org, 91–107.Google Scholar
- Florian Richter, Andrea Maldonado, Ludwig Zellner, and Thomas Seidl. 2020. OTOSO: Online trace ordering for structural overviews. In Process Mining Workshops (ICPM’20), S. Leemans and H. Leopold (Eds). Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_17Google Scholar
- Florian Richter and Thomas Seidl. 2017. TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. In Business Process Management (BPM’17), J. Carmona, G. Engels, A. Kumar (Eds.). Lecture Notes in Computer Science, vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_17Google Scholar
- Florian Richter and Thomas Seidl. 2019. Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. Information Systems 84 (2019), 265–282. https://www.sciencedirect.com/science/article/abs/pii/S030643791830019X?via%3Dihub.Google ScholarDigital Library
- Helen Schonenberg, Ronny Mans, Nick Russell, Nataliya Mulyar, and Wil van der Aalst. 2008. Process flexibility: A survey of contemporary approaches. In Advances in Enterprise Engineering. Jan L. G. Dietz, Antonia Albani, and Joseph Barjis (Eds.), Springer, Berlin, 16–30.Google Scholar
- Erich Schubert, Michael Weiler, and Hans-Peter Kriegel. 2014. SigniTrend: Scalable detection of emerging topics in textual streams by hashed significance thresholds. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, 871–880.Google ScholarDigital Library
- Alexander Seeliger, Timo Nolle, and Max Mühlhäuser. 2017. Detecting concept drift in processes using graph metrics on process graphs. In Proceedings of the 9th Conference on Subject-Oriented Business Process Management. Association for Computing Machinery, New York, NY, Article 6, 10 pages.Google ScholarDigital Library
- Florian Stertz and Stefanie Rinderle-Ma. 2018. Process histories—detecting and representing concept drifts based on event streams. In On the Move to Meaningful Internet Systems—OTM (Lecture Notes in Computer Science). H. Panetto, C. Debruyne, H. Proper, C. Ardagna, D. Roman, and R. Meersman (Eds.), Vol. 11229, Springer, 318–335.Google Scholar
- Florian Stertz and Stefanie Rinderle-Ma. 2019. Detecting and identifying data drifts in process event streams based on process histories. In Information Systems Engineering in Responsible Information Systems. Vol. 350, Springer, 133–144.Google Scholar
- Gabriel Marques Tavares, Paolo Ceravolo, Victor G. Turrisi Da Costa, Ernesto Damiani, and Sylvio Barbon Jr. 2019. Overlapping analytic stages in online process mining. In Proceedings of the 2019 IEEE International Conference on Services Computing. IEEE, 167–175.Google Scholar
- Charles Truong, Laurent Oudre, and Nicolas Vayatis. 2020. Selective review of offline change point detection methods. Signal Processing 167 (2020), 107299. https://www.sciencedirect.com/science/article/abs/pii/S0165168419303494?via%3Dihub.Google ScholarDigital Library
- Wil Van der Aalst. 2016. Process Mining: Data Science in Action. Springer, Berlin. 1–467.Google ScholarCross Ref
- Wil van der Aalst, Arya Adriansyah, Ana Karla Alves de Medeiros, Franco Arcieri, Thomas Baier, Tobias Blickle, Jagadeesh Chandra Bose, Peter van den Brand, Ronald Brandtjen, Joos Buijs, Andrea Burattin, Josep Carmona, Malu Castellanos, Jan Claes, Jonathan Cook, Nicola Costantini, Francisco Curbera, Ernesto Damiani, Massimiliano de Leoni, Pavlos Delias, Boudewijn F. van Dongen, Marlon Dumas, Schahram Dustdar, Dirk Fahland, Diogo R. Ferreira, Walid Gaaloul, Frank van Geffen, Sukriti Goel, Christian Günther, Antonella Guzzo, Paul Harmon, Arthur ter Hofstede, John Hoogland, Jon Espen Ingvaldsen, Koki Kato, Rudolf Kuhn, Akhil Kumar, Marcello La Rosa, Fabrizio Maggi, Donato Malerba, Ronny S. Mans, Alberto Manuel, Martin McCreesh, Paola Mello, Jan Mendling, Marco Montali, Hamid R. Motahari-Nezhad, Michael zur Muehlen, Jorge Munoz-Gama, Luigi Pontieri, Joel Ribeiro, Anne Rozinat, Hugo Seguel Pérez, Ricardo Seguel Pérez, Marcos Sepúlveda, Jim Sinur, Pnina Soffer, Minseok Song, Alessandro Sperduti, Giovanni Stilo, Casper Stoel, Keith Swenson, Maurizio Talamo, Wei Tan, Chris Turner, Jan Vanthienen, George Varvaressos, Eric Verbeek, Marc Verdonk, Roberto Vigo, Jianmin Wang, Barbara Weber, Matthias Weidlich, Ton Weijters, Lijie Wen, Michael Westergaard, and Moe Wynn. 2011. Process mining manifesto. In Business Process Management Workshops. F. Daniel, K. Barkaoui, and S. Dustdar (Eds.), Vol. 99. Springer, 169–194.Google Scholar
- W. M. P. Van Der Aalst, M. Pesic, and H. Schonenberg. 2009. Declarative workflows: Balancing between flexibility and support. Computer Science—Research and Development 23, 2 (2009), 99–113.Google Scholar
- W. M. P. van der Aalst, B. F. van Dongen, C. Günther, A. Rozinat, H. M. W. Verbeek, and A. J. M. M. Weijters. 2009. Prom: The process mining toolkit. In Proceedings of the Business Process Management Demonstration Track. Vol. 489. CEUR-WS.org, 1–4.Google Scholar
- R. J. van Van and U. Goltz. 1989. Equivalence notions for concurrent systems and refinement of actions. In 14th Symposium on Mathematical Foundations of Computer Science. A. Kreczmar and G. Mirkowska (Eds.), Vol. 379. Springer, Berlin, 237–248.Google Scholar
- Sebastiaan J. van Zelst, Boudewijn F. van Dongen, and Wil M. P. van der Aalst. 2018. Event stream-based process discovery using abstract representations. Knowledge and Information Systems 54, 2 (2018), 407–435.Google ScholarDigital Library
- Barbara Weber, Manfred Reichert, and Stefanie Rinderle-Ma. 2008. Change patterns and change support features - Enhancing flexibility in process-aware information systems. Data and Knowledge Engineering 66, 3 (2008), 438–466.Google ScholarDigital Library
- Matthias Weidlich, Jan Mendling, and Mathias Weske. 2011. Efficient consistency measurement based on behavioral profiles of process models. IEEE Transactions on Software Engineering 37, 3 (2011), 410–429.Google ScholarDigital Library
- A. J. M. M. Weijters, W. M. P. Van Der Aalst, and A. K. Alves De Medeiros. 2006. Process Mining with the HeuristicsMiner Algorithm. Technische Universiteit Eindhoven, Eindhoven.Google Scholar
- Claes Wohlin. 2014. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. ACM Press, New York, NY, 1–10.Google ScholarDigital Library
- H. Yang, L. Wen, and J. Wang. 2012. An approach to evaluate the local completeness of an event log. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining. IEEE, 1164–1169.Google Scholar
- Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, and Artem Polyvyanyy. 2019. Comprehensive process drift analysis with the visual drift detection tool. In Proceedings of the CEUR Workshop Proceedings. CEUR-WS, 108–112.Google Scholar
- Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, and Artem Polyvyanyy. 2019. Comprehensive process drift detection with visual analytics. In Conceptual Modeling ER 2019. A. Laender, B. Pernici, E. P. Lim, and J. de Oliveira (Eds.), Springer, 119–135.Google Scholar
- Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, and Artem Polyvyanyy. 2021. Visual drift detection for sequence data analysis of business processes. IEEE Transactions on Visualization and Computer Graphics (2021), 1–1. https://ieeexplore.ieee.org/document/9316994.Google Scholar
- Anton Yeshchenko, Jan Mendling, Claudio Di Ciccio, and Artem Polyvyanyy. 2020. VDD: A visual drift detection system for process mining. In Proceedings of the Doctoral Consortium and Tool Demo Track. CEUR-WS.org, Padua, 31–34.Google Scholar
- Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, and Thomas Seidl. 2020. Concept drift detection on streaming data with dynamic outlier aggregation. In Process Mining Workshops—1st International Workshop on Streaming Analytics for Process Mining, Springer (Ed.). Springer, Padua, 189–192.Google Scholar
- Haiping Zha, Jianmin Wang, Lijie Wen, Chaokun Wang, and Jiaguang Sun. 2010. A workflow net similarity measure based on transition adjacency relations. Computers in Industry 61, 5 (Jun. 2010), 463–471.Google ScholarDigital Library
- C. Zheng, L. Wen, and J. Wang. 2017. Detecting process concept drifts from event logs. In On the Move to Meaningful Internet Systems (OTM’17), Panetto H. et al. (Eds). Lecture Notes in Computer Science, vol 10573. Springer, Cham. https://doi.org/10.1007/978-3-319-69462-7_33Google ScholarDigital Library
Index Terms
- A Survey on Concept Drift in Process Mining
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