PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes |
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Authors: | Khadijah Muzzammil Hanga Yevgeniya Kovalchuk Mohamed Medhat Gaber |
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Institution: | 1.School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK;2.Department of Computer Science, University of Reading, Reading RG6 6DH, UK;3.Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt |
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Abstract: | This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method. |
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Keywords: | process mining business process management graph streams concept drift detection concept drift localisation deep learning long short-term memory |
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