New method for mining invisible tasks in service process based on probability behavior relationship

Provided is a new method for mining invisible tasks in a service process based on Petri net probability behavior relationship. First, a recorded log sequence is partitioned, and an activity transition set Sigma is divided into disjoint non-void subsets Sigma(1), Sigma(2),...,Sigma(n). Then, the partitioning portion is segmented in a binary manner to find out the relationship between activity pairs, the relationship with the highest probability is taken as the behavior relationship between partitions, and a source model is built. Next, a sequence with default activities in the log is analyzed, and the source model is optimized to find out a model with invisible tasks. Finally, the model is compared with the source model to obtain the rationality and appropriateness of the model with invisible tasks, and the positions of the invisible tasks in the service process are analyzed through comparison. By using the existing method for mining invisible tasks, a model with invisible tasks cannot be mined out efficiently. By using the new method of the invention, invisible tasks in an incomplete log can be mined out, and a service process model with invisible tasks can be mined out.