8 research outputs found
BINet: Multi-perspective Business Process Anomaly Classification
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 the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets
Analyzing Business Process Anomalies Using Autoencoders
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 intelligence, classic anomaly detection is not very
frequently researched. In this paper, we propose a method, using autoencoders,
for detecting and analyzing anomalies occurring in the execution of a business
process. Our method does not rely on any prior knowledge about the process and
can be trained on a noisy dataset already containing the anomalies. We
demonstrate its effectiveness by evaluating it on 700 different datasets and
testing its performance against three state-of-the-art anomaly detection
methods. This paper is an extension of our previous work from 2016 [30].
Compared to the original publication we have further refined the approach in
terms of performance and conducted an elaborate evaluation on more
sophisticated datasets including real-life event logs from the Business Process
Intelligence Challenges of 2012 and 2017. In our experiments our approach
reached an F1 score of 0.87, whereas the best unaltered state-of-the-art
approach reached an F1 score of 0.72. Furthermore, our approach can be used to
analyze the detected anomalies in terms of which event within one execution of
the process causes the anomaly.Comment: 20 pages, 5 figure
Analyzing Business Process Anomalies Using Autoencoders
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 intelligence, classic anomaly detection is not very
frequently researched. In this paper, we propose a method, using autoencoders,
for detecting and analyzing anomalies occurring in the execution of a business
process. Our method does not rely on any prior knowledge about the process and
can be trained on a noisy dataset already containing the anomalies. We
demonstrate its effectiveness by evaluating it on 700 different datasets and
testing its performance against three state-of-the-art anomaly detection
methods. This paper is an extension of our previous work from 2016 [30].
Compared to the original publication we have further refined the approach in
terms of performance and conducted an elaborate evaluation on more
sophisticated datasets including real-life event logs from the Business Process
Intelligence Challenges of 2012 and 2017. In our experiments our approach
reached an F1 score of 0.87, whereas the best unaltered state-of-the-art
approach reached an F1 score of 0.72. Furthermore, our approach can be used to
analyze the detected anomalies in terms of which event within one execution of
the process causes the anomaly.Comment: 20 pages, 5 figure
Learning of Process Representations Using Recurrent Neural Networks
In process mining, many tasks use a simplified representation of a single case to perform tasks like trace clustering, anomaly detection, or subset identification. These representations may capture the control flow of the process as well as the context a case is executed in. However, most of these representations are hand-crafted, which is very time-consuming for practical use, and the incorporation of event and case attributes as contextual factors is challenging. In this paper, we propose a neural network architecture for representation learning to automate the generation. Our network is trained in a supervised fashion to learn the most meaningful features to obtain highly dense and accurate vector representations of cases of an event log. We implemented our approach and conducted experiments in the context of trace clustering with publicly available event logs to show its applicability. The results show improvements regarding the separation of cases, and that process models discovered from identified subsets are of high quality