Top

Published in:

2020 | OriginalPaper | Chapter

Lambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders

Authors : Philippe Krajsic, Bogdan Franczyk

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The analysis of event data in the context of process mining is becoming increasingly important. In particular, the processing of streaming data in the sense of an real-time analysis is gaining in relevance. More and more fields of application are emerging in which an operational support becomes necessary, i.e. in surgery or manufacturing. For proper analysis a cleanup of the streaming data in a pre-processing step is necessary to ensure accurate process mining activities. This paper presents a blueprint of a lambda architecture in which an autoencoder is embedded that is supposed to allow unsupervised anomaly detection in event streams, like incorrect traces, events and attributes, and thus will help to improve results in online process mining.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter An Efficient Approach for Improving Recursive Joins Based on Three-Way Joins in Spark

next chapter Biomedical Text Recognition Using Convolutional Neural Networks: Content Based Deep Learning

van der Aalst, W.: Process Mining: Data Science in Action, 2nd edn. Springer, Berlin (2016). https://doi.org/10.1007/978-3-662-49851-4CrossRef

van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRef

Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17CrossRef

Hassani, M., Sicca, S., Richter, F., Seidl, T.: Efficient process discovery from event streams using sequential pattern matching. In: IEEE Proceedings of SSCI 2015 (2015)

Maaradji, A., Dumas, M., La Rosa, M., Ostovar, A.: Detecting sudden and gradual drifts in business processes from execution traces. IEEE Trans. Knowl. Data Eng. 29(10), 2140–2154 (2017)CrossRef

Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.V.: Detecting drift from event streams of unpredictable business processes. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 330–346. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46397-1_26CrossRef

Ghionna, L., Greco, G., Guzzo, A., Pontieri, L.: Outlier detection techniques for process mining applications. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 150–159. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68123-6_17CrossRef

Wang, J., Song, S., Lin, X., Zhu, X., Pei, J.: Cleaning structured event logs: a graph repair approach. In: 31st IEEE International Conference on Data Engineering (2015)

Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out infrequent behavior from business process event logs. IEEE Trans. Knowl. Data Eng. 29(2), 300–314 (2017)CrossRef

10.

Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875–1893 (2018). https://doi.org/10.1007/s10994-018-5702-8MathSciNetCrossRefMATH

11.

Sani, M.F., van Zelst, S.J., van der Aalst, W.M.P.: Improving process discovery results by filtering outliers using conditional behavioural probabilities. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 216–229. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_16CrossRef

12.

van Zelst, S.J., Fani Sani, M., Ostovar, A., Conforti, R., La Rosa, M.: Filtering spurious events from event streams of business processes. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 35–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_3CrossRef

13.

Marz, N., Warren, J.: Big Data. Principles and Best Practices of Scalable Realtime Data Systems, Manning (2013)

14.

Hoseinyfarahabady, M., Taheri, J., Tari, Z., Zomaya, A.Y.: A dynamic resource controller for a lambda architecture. In: IEEE Proceedings of the International Conference on Parallel Processing (2017)

15.

Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S.S.: Lambda architecture for cost-effective batch and speed big data processing. In: IEEE Proceedings International Conference on Big Data (2015)

16.

Batyuk, A., Voityshyn, V.: Streaming process discovery for lambda architecture-based process monitoring platform. In: IEEE 8th International Conference on Computer Science and Information Technology (2018)

17.

Batyuk, A., Voityshyn, V.: Apache storm based on topology for real-time processing of streaming data from social media. In: IEEE First International Conference on Data Stream Mining & Processing (2016)

18.

Batyuk, A., Voityshyn, V., Verhun, V.: Software architecture design of the real-time processes monitoring platform. In: IEEE Second International Conference on Data Stream Mining & Processing (2018)

19.

Kreps, J.: Questioning the Lambda Architecture. O’Reilly.com (2014). https://www.oreilly.com/radar/questioning-the-lambda-architecture. Accessed 27 Feb 2020

20.

Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning. Association for Computing Machinery (2010)

21.

Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the SDM. SIAM (2007)

22.

Cormode, G., Shkapenyuk, V., Srivastava, D., Xu, B.: Forward decay: a practical time decay model for streaming systems. In: IEEE Proceedings of the ICDE (2009)

23.

Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

24.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:abs/1412.6980 (2014)

25.

Augenstein, C., Spangenberg, N., Franczyk, B.: Applying machine learning to big data streams: an overview of challenges. In: 4th IEEE International Conference on Soft Computing and Machine Intelligence (2017)

26.

van Dongen, B.F.: Dataset BPI challenge 2019. 4TU. Centre for Research Data (2019). https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1

Title: Lambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders
Authors: Philippe Krajsic
Bogdan Franczyk
Publisher: Springer International Publishing
Book: Advances in Computational Collective Intelligence
Print ISBN: 978-3-030-63118-5

Electronic ISBN: 978-3-030-63119-2

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63119-2_47

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner