Near-real-time monitoring and classification of business process event streams is becoming more and more prominent. This also includes ensuring data quality for the application of downstream online process mining activities and therefore identify and classify incorrect process behavior of incoming event streams in an online setting, what is considered too little in existing approaches. In this paper, we present an online classification approach that supports monitoring and anomaly detection in event streams at the event level. Possible process drifts can be handled by an online learning workflow. By integrating two explanatory components, the results of the online classification are made transparent and comprehensible. Through a technical experiment, the performance of the classification approach is evaluated based on different data sets. Thereby, the classification model achieves an average F1 score of 0.877 with an average processing time of ∼15 ms per event.
%0 Journal Article
%1 Krajsic2022
%A Krajsic, Philippe
%A Franczyk, Bogdan
%D 2022
%J Procedia Computer Science
%K business classification, explainability learning, online process,
%P 235-244
%R https://doi.org/10.1016/j.procs.2022.09.056
%T Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams
%U https://www.sciencedirect.com/science/article/pii/S1877050922009292
%V 207
%X Near-real-time monitoring and classification of business process event streams is becoming more and more prominent. This also includes ensuring data quality for the application of downstream online process mining activities and therefore identify and classify incorrect process behavior of incoming event streams in an online setting, what is considered too little in existing approaches. In this paper, we present an online classification approach that supports monitoring and anomaly detection in event streams at the event level. Possible process drifts can be handled by an online learning workflow. By integrating two explanatory components, the results of the online classification are made transparent and comprehensible. Through a technical experiment, the performance of the classification approach is evaluated based on different data sets. Thereby, the classification model achieves an average F1 score of 0.877 with an average processing time of ∼15 ms per event.
@article{Krajsic2022,
abstract = {Near-real-time monitoring and classification of business process event streams is becoming more and more prominent. This also includes ensuring data quality for the application of downstream online process mining activities and therefore identify and classify incorrect process behavior of incoming event streams in an online setting, what is considered too little in existing approaches. In this paper, we present an online classification approach that supports monitoring and anomaly detection in event streams at the event level. Possible process drifts can be handled by an online learning workflow. By integrating two explanatory components, the results of the online classification are made transparent and comprehensible. Through a technical experiment, the performance of the classification approach is evaluated based on different data sets. Thereby, the classification model achieves an average F1 score of 0.877 with an average processing time of ∼15 ms per event.},
added-at = {2024-09-10T11:54:51.000+0200},
author = {Krajsic, Philippe and Franczyk, Bogdan},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/22440f76f3a9cd26d7544d740d9d2c601/scadsfct},
doi = {https://doi.org/10.1016/j.procs.2022.09.056},
interhash = {2ff2108a68c71edb8bb143e0c799989f},
intrahash = {2440f76f3a9cd26d7544d740d9d2c601},
issn = {1877-0509},
journal = {Procedia Computer Science},
keywords = {business classification, explainability learning, online process,},
note = {Knowledge-Based and Intelligent Information \& Engineering Systems: Proceedings of the 26th International Conference KES2022},
pages = {235-244},
timestamp = {2024-09-10T11:54:51.000+0200},
title = {Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922009292},
volume = 207,
year = 2022
}
