DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks

2 code implementations • 29 Nov 2019 • Timo Nolle, Alexander Seeliger, Nils Thoma, Max Mühlhäuser

In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search.

BINet: Multi-perspective Business Process Anomaly Classification

3 code implementations • 8 Feb 2019 • Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser

Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification.

Anomaly Classification Anomaly Detection +3

Analyzing Business Process Anomalies Using Autoencoders

no code implementations • 3 Mar 2018 • Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser

In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process.

Anomaly Detection