Volume 17, Issue 4 (2-2021)                   JSDP 2021, 17(4): 33-48 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

khojasteh F, Kahani M, Behkamal B. Concept drift detection in business process logs using deep learning. JSDP. 2021; 17 (4) :33-48
URL: http://jsdp.rcisp.ac.ir/article-1-912-en.html
Departeman Computer Engineering, Ferdowsi University of Mashhad
Abstract:   (873 Views)
Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques cannot capture such “second-order dynamics” and analyze these processes as if they are in steady-state. Such changes can significantly impact the performance of processes. Hence, for the process management, it is crucial that changes in processes be discovered and analyzed. Process change detection is also known as business process drift detection.
All the existing methods for process drift detection are dependent on the size of windows used for detecting changes. Identifying convenient features that characterize the relations between traces or events is another challenge in most methods. In this thesis, we propose an automated and window-independent approach for detecting sudden business process drifts by introducing the notion of trace embedding. Using trace embedding makes it possible to automatically extract all features from the relations between traces. We show that the proposed approach outperforms all the existing methods in respect of its significantly higher accuracy and lower detection delay.
Full-Text [PDF 4803 kb]   (171 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2018/10/10 | Accepted: 2019/09/2 | Published: 2021/02/22 | ePublished: 2021/02/22

References
1. [1] W. Van Der Aalst, A. Adriansyah, A. K. A. De Medeiros, F. Arcieri, T. Baier, T. Blickle, et al., "Process mining manifesto", in International Conference on Business Process Management, vol. 37 No.3, pp. 169-194, 2011.
2. [2] M. U. Lavanya and M. S. K. Talluri, "Dealing With Concept Drifts In Process Mining Using Event Logs", International Journal Of Engineering And Computer Science, vol. 4, pp. 13433-13437, 2015.
3. [3] R. J. C. Bose, W. M. Van Der Aalst, I. Žliobaitė, and M. Pechenizkiy, "Dealing with concept drifts in process mining", IEEE transactions on neural networks and learning systems, vol. 25 No.1, pp. 154-171, 2014. [DOI:10.1109/TNNLS.2013.2278313] [PMID]
4. [4] J. C. Schlimmer and R. H. Granger, "Beyond Incremental Processing: Tracking Concept Drift", the Association for the Advancement of Artificial Intelligence, pp. 502-507, 1986.
5. [5] R. J. C. Bose, W. M. van der Aalst, I. Žliobaitė, and M. Pechenizkiy, "Handling concept drift in process mining", 23rd International Conference on Advanced Information Systems Engineering(CAiSE), London, UK, Springer, pp. 391-405, 2011. [DOI:10.1007/978-3-642-21640-4_30]
6. [6] H. Schonenberg, R. Mans, N. Russell, N. Mulyar, and W. van der Aalst, "Process flexibility: A survey of contemporary approaches", 4th International Workshop on Advances in Enterprise Engineering I, Springer, pp. 16-30, 2008. [DOI:10.1007/978-3-540-68644-6_2]
7. [7] J. Martjushev, R. J. C. Bose, and W. M. van der Aalst, "Change Point Detection and Dealing with Gradual and Multi-order Dynamics in Process Mining", 14th International Conference on Business Informatics Research, Tartu, Estonia, Springer, pp. 161-178, 2015. [DOI:10.1007/978-3-319-21915-8_11]
8. [8] R. Accorsi and T. Stocker, "Discovering workflow changes with time-based trace clustering" , International Symposium on Data-Driven Process Discovery and Analysis, Italy, Springer, pp. 154-168, 2011. [DOI:10.1007/978-3-642-34044-4_9]
9. [9] B. Hompes, J. Buijs, W. van der Aalst, P. Dixit, and J. Buurman, 2015, "Detecting Change in Processes Using Comparative Trace Clustering", the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA), Vienna, Austria , pp. 95-108.
10. [10] J. Carmona and R. Gavalda, "Online techniques for dealing with concept drift in process mining", 11th International Symposium on Intelligent Data Analysis, Finland, Springer, pp.90-102, 2012. [DOI:10.1007/978-3-642-34156-4_10]
11. [11] P. Weber, B. Bordbar, and P. Tiño, "Real-Time Detection of Process Change using Process Mining", ICCSW, United Kingdom, 2011.
12. [12] A. Maaradji, M. Dumas, M. La Rosa, and A. Ostovar, "Fast and accurate business process drift detection", the 13th International Conference on Business Process Management, Austria, Springer, pp. 406-422, 2015. [DOI:10.1007/978-3-319-23063-4_27]
13. [13] A. Maaradji, M. Dumas, M. L. Rosa, and A. Ostovar, "Detecting Sudden and Gradual Drifts in Business Processes from Execution Traces", IEEE Trans. Knowl. Data Eng., vol. 29, pp. 2140-2154, 2017. [DOI:10.1109/TKDE.2017.2720601]
14. [14] T. Li, T. He, Z. Wang, Y. Zhang, and D. Chu, "Unraveling Process Evolution by Handling Concept Drifts in Process Mining", in Services Computing (SCC), IEEE International Conference on, pp. 442-449, 2017. [DOI:10.1109/SCC.2017.63]
15. [15] A. Seeliger, T. Nolle, and M. Mühlhäuser, "Detecting Concept Drift in Processes using Graph Metrics on Process Graphs", Proceedings of the 9th Conference on Subject-oriented Business Process Management, 2017. [DOI:10.1145/3040565.3040566]
16. [16] M. Baroni, G. Dinu, and G. Kruszewski, "Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors", ACL (1), pp. 238-247, 2014. [DOI:10.3115/v1/P14-1023]
17. [17] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by latent semantic analysis," Journal of the American society for information science, vol.41, p. 391, 1990. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 [DOI:10.1002/(SICI)1097-4571(199009)41:63.0.CO;2-9]
18. [18] A. Mandelbaum and A. Shalev, "Word Embeddings and Their Use In Sentence Classification Tasks," arXiv preprint arXiv:1610.08229, 2016.
19. [19] T. Mikolov, W.-t. Yih, and G. Zweig, "Linguistic regularities in continuous space word representations", HLT-NAACL, Atalanta, USA, pp.746-751, 2013.
20. [20] Q. V. Le and T. Mikolov, "Distributed Representations of Sentences and Documents", ICML, Beijing, China, pp. 1188-1196, 2014.
21. [21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space", arXiv preprint arXiv:1301.3781, 2013.
22. [22] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality", the 27th Conference on Neural Information Processing Systems(NIPS), Nevada, United States, pp. 3111-3119, 2013.
23. [23] X. Rong, "word2vec parameter learning explained", arXiv preprint arXiv:1411.273, 2014.
24. [24] P. Ristoski and H. Paulheim, "Rdf2vec: Rdf graph embeddings for data mining", 15th International Semantic Web Conference(ISWC), Kobe, Japan, pp. 498-514, 2016. [DOI:10.1007/978-3-319-46523-4_30]
25. [25] M. Rahman, "Applications of Fourier transforms to generalized functions", WIT Press, 2011.
26. [26] S.-S. Ho, "A martingale framework for concept change detection in time-varying data streams", the 22nd international conference on Machine learning, Germany, ACM, pp. 321-327, 2005.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing