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Trzcionkowska Agnieszka (AGH University of Science and Technology Kraków, Poland), Brzychczy Edyta (AGH University of Science and Technology Kraków, Poland)
Practical Aspects of Event Logs Creation for Industrial Process Modelling
Multidisciplinary Aspects of Production Engineering, 2018, vol. 1, s. 77-83, rys., tab., bibliogr. 13 poz.
Słowa kluczowe
Eksploatacja górnicza, Górnictwo, Maszyny i urządzenia
Mining exploitation, Mining sector, Machinery and equipment
streszcz., summ.
In the paper we address the challenge of applying process mining techniques for discovering models of underground mining operations based on a sensor data. The paper presents practical approach of creation an event log based on industrial sensors data gathered in an underground mine monitoring systems. The proposed approach enables to generate event logs at different generalization levels based on several numbers of discovered stages of devices performance. For discovering process stages data mining techniques such as exploratory data analysis, clustering and classification have been applied. Created event log has been used in one of the process mining tasks - process model discovery. (original abstract)
Pełny tekst
  1. van der Aalst, W.M.P. (2018). Process Discovery from Event Data: Relating Models and Logs Through Abstractions. WIREs Data Mining and Knowledge Discovery, to appear. [Accessed 30 May. 2018].
  2. van der Aalst, W.M.P. (2016). Process Mining: Data Science in Action. Berlin: Springer-Verlag.
  3. van der Aalst, W.M.P. (2013). Process Mining in the Large: A Tutorial. . Lecture Notes in Business Information Processing,172, pp. 33-76.
  4. Bose, R.P.J.C., van der Aalst, W.M.P. (2009). Abstractions in process mining: A taxonomy of patterns. Germany. 7 th International BPM Conference Proceedings, pp. 159-175.
  5. Brzychczy, E., Trzcionkowska, A. (2017). New possibilities for process analysis in an underground mine. In: Management in mining production, economic, social and technical perspectives and experiences. Zeszyty Naukowe. Organizacja i Zarządzanie, 111, pp. 13-25.
  6. Cook, D.J., Krishnan, N.C., Rashidi, P. (2013). Activity discovery and activity recognition: A new partnership. IEE T. Cybernetics. 43(3), pp. 820-828.
  7. van Eck, M.L., Sidorova, N., van der Aalst. W.M.P. (2016). Enabling Process Mining on Sensor Data from Smart Products. Brussels. IEEE RCIS. IEEE Computer Society Press, pp. 1-12 .
  8. Guenther, C.W., van der Aalst, W.M.P. (2006). Mining Activity Clusters from Low-Level Event Logs. Eindhoven. BETA Working Paper Series, WP 165.
  9. Hompes, B.F.A., Verbeek, van der Aalst, H.M.W. (2014). Finding Suitable Activity Clusters for Decomposed Process Discovery. Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), 1293, pp. 16-30.
  10. 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. International Conference on Business Process Management, pp.125-141.
  11. ProM (2016). Process Mining Group. Eindhoven Technical University. Available at:
  12. Rousseeuw, P., Silhouettes J. (1987). A graphical aid to the interpretation and validation of cluster analysis,. Journal of Computational and Applied Mathematics, 20, pp. 53-65.
  13. Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P. (2018). Event Abstraction for Process Mining Using Supervised Learning Techniques. In: Bi. Y. Cham, S. Kapoor, R. Bhatia, eds., Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. Lecture Notes in Networks and Systems,15, pp. 51-269.
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