ABSTRACT
Thoroughly documenting digital business processes in a company is a crucial and necessary, yet cumbersome task. However, having detailed documentation of one's processes in a modelling language like Business Process Model and Notation (BPMN) can prove very useful regarding process optimization or automation, employee training and on- and offboarding. Process and task mining frameworks try to ease the creation of process documentation by automatically generating it based on transaction or user interaction data with the system. These approaches often have the disadvantage of not covering the whole process due to a variety of possible execution paths and their habit of not continuously recording process data. We propose an extension to the task mining tool Desktop Activity Mining (DAM) which allows to capture data continuously over several hours and therefore not miss any important cases that might not occur very often. This approach also limits the influence of human errors when recording process data with certain frameworks for documentation purposes and provide the possibility of an improved degree of automation. We evaluate the approach on real-world data to show its feasibility and application in practice. We used a combination of already existing algorithms and created our own. By classifying 332 unique user interactions, we end up with 76 different equivalence classes. Evaluating the algorithm, we achieved a classification correctness of 70% in two datasets.
- A. Promitzer, S. Torabi-Goudarzi and D. Werth, "Objectively Assessing the Suitability of Digital Processes for Robotic Process Automation.," in 17. Internationale Tagung Wirtschaftsinformatik, 2022.Google Scholar
- C. Linn, P. Zimmermann and D. Werth, "Desktop activity mining-a new level of detail in mining business processes," in Workshops der INFORMATIK 2018-Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, 2018.Google Scholar
- R. Chen , “Dynamic order Markov model for categorical sequence clustering,” in Journal of Big Data 8, 2021Google ScholarCross Ref
- J. L. Hsu, A. L. Chen and H. C. Chen, "Finding Approximate Repeating Patterns from Sequence Data," in ISMIR, 2004.Google Scholar
- M. Walicki and D. R. Ferreira, "Sequence Partitioning for Process Mining with Unlabeled Event Logs," Data & Knowledge Engineering, vol. 70, no. 10, pp. 821-841, 2011.Google ScholarDigital Library
- Y. Kwon, W. Y. Lee, M. Balazinska and G. Xu, "Clustering events on streams using complex context information," in IEEE International Conference on Data Mining Workshops, 2008.Google Scholar
- A. A. Makanju, A. N. Zincir-Heywood and E. E. Milios, "Clustering event logs using iterative partitioning," in ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.Google Scholar
- R. Vaarandi, "A Data Clustering Algorithm for Mining Patterns From Event Logs," in IEEE Workshop on IP Operations & Management, 2003.Google Scholar
- A. K. A. de Medeiros, A. J. Weijters and W. M. van der Aalst, "Genetic process mining: an experimental evaluation," Data mining and knowledge discovery, vol. 14, no. 2, pp. 245-304, 2007.Google Scholar
- H. M. W. Verbeek and W. M. van der Aalst, "Decomposed Process Mining: The ILP Case," in International conference on business process management, 2014.Google Scholar
- C. W. Günther, A. Rozinat and W. M. Van Der Aalst, "Activity mining by global trace segmentation," in International Conference on Business Process Management, 2009.Google Scholar
- D. R. Ferreira and D. Gillblad, "Discovering process models from unlabelled event logs," in International Conference on Business Process Management, 2009.Google Scholar
- S. J. van Zelst, F. d. L. M. Mannhardt and A. Koschmider, "Event abstraction in process mining: literature review and taxonomy," Granular Computing, vol. 6, no. 3, 2021.Google Scholar
- L. T. Ly, C. Indiono, J. Mangler and S. Rinderle-Ma, "Data transformation and semantic log purging for process mining," in International Conference on Advanced Information Systems Engineering, 2012.Google Scholar
- A. Augusto, C. Raffaele, D. Marlon and M. La Rosa, "Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs," in Knowledge and Information Systems, 2019.Google Scholar
- S. Goedertier, J. De Weerdt D. Martens, J. Vanthienen, B. Baesens, "Process discovery in event logs: An application in the telecom industry," in Applied Soft Computing, 2011.Google Scholar
- A. Augusto. , "Automated Discovery of Process Models from Event Logs: Review and Benchmark," in IEEE Transactions on Knowledge and Data Engineering, 2019.Google ScholarDigital Library
- V. I. Levenshtein, "Binary codes capable of correcting deletions, insertions, and reversals," Soviet physics doklady, vol. 10, no. 8, pp. 707-710, 1966.Google Scholar
- P. Brémaud, "Discrete-Time Markov Chains," in Markov Chains, Springer, 2020, pp. 63-109.Google Scholar
Index Terms
- Extracting Process Instances from User Interaction Logs
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