METHOD OF CONSTRUCTING AN ATTRIBUTE DESCRIPTION OF THE BUSINESS PROCESS "AS IS" IN THE PROCESS APPROACH TO ENTERPRISE MANAGEMENT
The problem of constructing an attribute description of a business process with the automated formation of process models “as is” using logs of information systems in which the tracks of individual processes are not identified is considered. It is shown that to solve this problem, it is advisable to distinguish the distinctive properties of individual business processes represented by the attributes of log events. A method for constructing an attribute description of a business process is proposed. The method is based on the comparison of combinations of attributes for intervals of events of a fixed length and the subsequent selection of subsets of attributes with the same values. The method includes the steps of forming the intervals of events, constructing combinations of attributes for specified intervals, as well as calculating and subsequently averaging the weights of combinations of attributes on these intervals. The result of the method is a weight-ordered set of event attributes and their values, which takes into account the attribute and temporal aspects of the business process. The method creates conditions for a more efficient transition from functional to process management based on splitting the log into processes using the resulting attribute description and subsequent prototyping of business process models “as is” by means of process mining.
Dumas, M., La Rosa, M., Mendling, J., Reijers, H. A. (2013). Fundamentals of Business Process Management. Springer, 400. doi: https://doi.org/10.1007/978-3-642-33143-5
Van der Aalst, W. M. P. (2013). Business Process Management: A Comprehensive Survey. ISRN Software Engineering, 2013, 1–37. doi: https://doi.org/10.1155/2013/507984
Van der Aalst, W. (2016). Process Mining: Data Science in Action. Springer-Verlag, 467. doi: https://doi.org/10.1007/978-3-662-49851-4
Vom Brocke, J., Rosemann, M. (Eds.) (2015). Handbook on Business Process Management 1. Introduction, Methods, and Information Systems. Berlin: Springer-Verlag. doi: https://doi.org/10.1007/978-3-642-45100-3
Mannhardt, F., de Leoni, M., Reijers, H. A., van der Aalst, W. M. P., Toussaint, P. J. (2016). From Low-Level Events to Activities – A Pattern-Based Approach. Business Process Management, 125–141. doi: https://doi.org/10.1007/978-3-319-45348-4_8
Chalyi, S., Levykin, I., Petrychenko, A., Bogatov, I. (2018). Causality-based model checking in business process management tasks. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert.2018.8409176
Kalenkova, A. A., van der Aalst, W. M. P., Lomazova, I. A., Rubin, V. A. (2015). Process mining using BPMN: relating event logs and process models. Software & Systems Modeling, 16 (4), 1019–1048. doi: https://doi.org/10.1007/s10270-015-0502-0
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W. M. P. (2016). Mining local process models. Journal of Innovation in Digital Ecosystems, 3 (2), 183–196. doi: https://doi.org/10.1016/j.jides.2016.11.001
Kalynychenko, O., Chalyi, S., Bodyanskiy, Y., Golian, V., Golian, N. (2013). Implementation of search mechanism for implicit dependences in process mining. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). doi: https://doi.org/10.1109/idaacs.2013.6662657
Van der Aalst W. M. P. (2013). A General Divide and Conquer Approach for Process Mining. Federated Conference on Computer Science and Information Systems (FedCSIS 2013). Available at: http://www.processmining.org/_media/blogs/pub2013/2-fedcis-wvdaalst-keynote.pdf
Van der Aalst, W. M. P. (2018). Process discovery from event data: Relating models and logs through abstractions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8 (3), e1244. doi: https://doi.org/10.1002/widm.1244
Chalyi, S. F., Bogatov, E. O., Pribylnova, I. B. (2018). Techniques of reordering traces in the event logs in business process management tasks. Bulletin of National Technical University “KhPI”. Series: System Analysis, Control and Information Technologies, 21, 43–47. doi: https://doi.org/10.20998/2079-0023.2018.21.08
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