Explainable Business Process Remaining Time Prediction Using Reachability Graph

Abstract

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph, which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next, the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

Sampling business process event logs with guarantees

“…This discipline encompasses three primary types of process mining tasks: model discovery, 3–5 conformance checking, 6–8 and process enhancement 9–11 . In addition, process mining also includes sub domains such as process prediction 11–13 and business process automation 14 . Among these tasks, model discovery, which involves uncovering descriptive process models from event logs, stands out as one of the most challenging.…”