关键词: 剩余时间预测, 波动性, 过程挖掘, 实时数据

Abstract: Remaining time prediction is a core task of business process predictive monitoring.Existing predictive models are usually built from offline,so it usually takes a long time to build or update the models which tend to degrade with streaming events.For this reason,a method of online prediction model construction and remaining time prediction was proposed.Based on hybrid transition systems that incorporate multiple abstract mechanisms,an incremental prediction model construction algorithm was proposed to ensure the model up to date.The prediction confidence was defined to represent the qualities of the prediction results,and a confidence-based prediction algorithm was proposed.The prediction algorithm expanded the candidate set of prediction values by looking back the history to improve the prediction accuracy.The volatility of prediction was defined to measure the temporal consistence of prediction.Experiments on multiple public data sets had been conducted,and the results showed that the proposed method outperformed the existing methods in terms of precision and volatility.

Key words: remaining time prediction, volatility, process mining, real-time data