Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times
作者：
Highlights：
• Utilization of event interim times for process analysis.
• Adaptation of a scalable and noise adapting trend detection method from text mining.
• Anytime drift detection in events streams for operational support.
• Visualization for supervised domain expert interaction.
• Application of Tesseract in the remaining time prediction.
摘要
•Utilization of event interim times for process analysis.•Adaptation of a scalable and noise adapting trend detection method from text mining.•Anytime drift detection in events streams for operational support.•Visualization for supervised domain expert interaction.•Application of Tesseract in the remaining time prediction.
论文关键词：Process mining,Event streams,Temporal drift detection,Operational support,Remaining time prediction
论文评审过程：Received 16 January 2018, Revised 5 October 2018, Accepted 9 November 2018, Available online 14 November 2018, Version of Record 18 June 2019.
论文官网地址：https://doi.org/10.1016/j.is.2018.11.003
