A Method for Discovery of Repeated Task Process Based on Equivalence Class

Abstract

The invention discloses a method for discovering repetitive task processes based on equivalence classes. By extending the definition of event sequence relations, a judgment theorem for equivalence classes to divide the same task subset is proposed. Events are divided into subsets of the same task equivalence class. In the processing stage, a short cycle process discovery algorithm is used to generate the WF-net model. In the post-processing stage, different subsets of the same task are renamed to achieve the goal of eliminating duplicate tasks. It first reads the standard business process XES file, uses sequence dependency definition and decision theorem to detect repetitive tasks, and outputs a business process model represented by WF‑net. This method has a high ability to solve problems involving short cycle structure, multi-predecessor-successor and repeated task process discovery, and this method can ensure that the mining results are correct and reasonable.

Description

technical field [0001] The invention relates to the field of data processing, more specifically, to a method for discovering repetitive task processes based on equivalence classes. Background technique [0002] At present, various information systems based on distributed computing, Internet of Things, and service computing environments all have a large amount of log data that records the process of business execution. Process mining technology analyzes these logs to discover valuable knowledge and help improve the original business process. Process discovery is the core technology of process mining, that is, how to discover business process models from logs. Usually business models include control flow structures such as sequential, parallel, selective, cyclic, non-free choice, implicit tasks, and repeated tasks. The ability to mine control flow structure, that is, to construct a process model that can describe the relationship between activity execution from event logs, i...

AI Technical Summary

Problems solved by technology

[0006] The above-mentioned method 1 cannot solve the problem of repetitive task recognition that contains short loop structures, because the heuristic rules of machine learning are not capable enough, and because no formal theory is used to represent the generative model, the mining results may be unreasonable; method 2 expands the heuristic rules to identify non-free choice structures, but still does not solve the shortcomings of method 1; methods 3 and 4 can only deal with short loops and repetitive task discovery problems in some cases, because they do not consider the situation where repeated tasks appear in different execution trajectories, and there is no Handle siege cases with multiple predecessors and successors

Images