GAMA: A Multi-graph-based Anomaly Detection Framework for Business
Processes via Graph Neural Networks
Abstract
Anomalies in business processes are inevitable for various reasons such
as system failures and operator errors. Detecting anomalies is important
for the management and optimization of business processes. In this
paper, we propose a multi-Graph based Anomaly
detection fraMework for business processes via grAph
neural networks, named GAMA. GAMA makes use of structural process
information and attribute information in a more integrated way. In GAMA,
multiple graphs are applied to model a trace in which each attribute is
modelled as a separate graph. In particular, the graph constructed for
the special attribute activity reflects the control flow. Then
GAMA employs a multi-graph encoder and a multi-sequence decoder on
multiple graphs to detect anomalies in terms of the reconstruction
errors. Moreover, three teacher forcing styles are designed to enhance
GAMA’s ability to reconstruct normal behaviours and thus improve
detection performance. We conduct extensive experiments on both
synthetic logs and real-life logs. The experiment results demonstrate
that GAMA outperforms state-of-the-art methods for both trace-level and
attribute-level anomaly detection.