Business Process Next Activity Prediction Method Utilizing Remove Marked Case Reorganization and Fine‐Grained Image Cube Action Engine

ABSTRACT Predictive business process monitoring discovers anomalies in business execution by predicting the next activity of a business process in real time, thereby helping enterprises to adjust and optimize business processes in a timely manner. Existing research usually focuses on the sequence information of a single trace in event logs or the structural information of process models, while ignoring the contextual correlation information in the process and the impact of existing and potential operational conflicts on the accuracy of the next activity prediction. To address these issues, we propose a next activity prediction method that combines trace case reorganization and expansion with a fine‐grained image cube constraint action engine. This method addresses the problem of limited case numbers in a single trace and the lack of sparse pixel information in the encoded image. First, the labels of cases are removed, and cases are reorganized based on context dependencies, expanding the number of cases in the trace. Then, Gramian Angular Field (GAF) is used for fine‐grained image encoding to enrich the content of the encoded image. A constraint cube constraint action engine is constructed, and Online Analytical Processing (OLAP) operations are used to constrain the process direction, monitor operational conflicts, and select the correct process direction. Finally, experimental results on four real event logs show that the proposed method outperforms the baseline methods in terms of the accuracy of next activity prediction.