Time Prediction Based on Process Mining Taking Concept Drift into Consideration

Abstract:

Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. Now many scholars have made a great contribution on predicting the completion time of running instances and a lot of algorithms have been proposed, but they mostly ignored concept drift which means the influence of the external factors. In order to improve the accuracy of the prediction, we take the concept drift into account. We do cluster analysis on the annotated transition system. Experiments show that our algorithm has a considerable degree of improvement over the previous.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2101-2105
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2101
• Online since: July 2014
• Authors: Fan Fang Huang*, Li Qun Zhang, Zhao Li
• Keywords: Cluster Analysis, Concept Drift, Process Mining, Time Prediction

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