Analyzing business process anomalies using autoencoders

Author:

Nolle TimoORCID,Luettgen Stefan,Seeliger Alexander,Mühlhäuser Max

Funder

LOEWE Initiative Hessen

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference45 articles.

1. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 .

2. Bezerra, F., & Wainer, J. (2013). Algorithms for anomaly detection of traces in logs of process aware information systems. Information Systems, 38(1), 33–44.

3. Bezerra, F., Wainer, J., & Van der Aalst, W. M. P. (2009). Anomaly detection using process mining. In T. Halpin, J. Krogstie, S. Nurcan, E. Proper, R. Schmidt, P. Soffer, & R. Ukor (Eds.), Enterprise, business-process and information systems modeling (pp. 149–161). Berlin, Heidelberg: Springer.

4. Böhmer, K., & Rinderle-Ma, S. (2016). Multi-perspective anomaly detection in business process execution events. In: OTM confederated international conferences On the move to meaningful internet systems (pp. 80–98). Springer.

5. Burattin, A. (2015). PLG2.: Multiperspective processes randomization and simulation for online and offline settings. CoRR abs/1506.0.

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