102 citations · 216 across the 20 of their papers we have counts for
3 papers · 1 filter
DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks
Timo Nolle, Alexander Seeliger, Nils Thoma +1
In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search. At the core…
BINet: Multi-perspective Business Process Anomaly Classification
Timo Nolle, Stefan Luettgen, Alexander Seeliger +1
In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both…
Analyzing Business Process Anomalies Using Autoencoders
Timo Nolle, Stefan Luettgen, Alexander Seeliger +1
Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business…