Distance-based parallel complete log mining method

Abstract

The invention discloses a distance-based parallel complete log mining method. Two algorithms are applied in a realization process of the method; the algorithm I is a potential causal relationship discovery algorithm; according to the algorithm I, through distance definition, a new implicit relationship discovery algorithm is established to solve a process model comprising selections; and the algorithm II is an AlphaParallelUseDistance algorithm, and can process a blocky model comprising a selection structure. Through the steps, a parallel complete log comprising fewer logs can be processed, mining is performed to obtain the process model with relatively high accuracy and relatively high conciseness, and the model structure is no longer only limited to the parallel blocky model. Compared with a mining result of an IM algorithm, a result of the method is higher in accuracy and more concise; and compared with an alpha|| algorithm, logs unminable for the algorithm can be processed, correctresults unobtainable for the algorithm can be obtained, and the application range is wider.

Description

technical field [0001] The invention belongs to the technical field of process mining, in particular to a distance-based concurrent complete log mining method. Background technique [0002] In process mining, the notion of completeness is important, as it represents the problem that the log contains too little data. Completeness assumes that all activities that may directly follow each other, directly follow each other in some trace in the log. This leads to the traditional partially complete log based on following relations, which requires a large number of traces in the log. [0003] When mining incomplete logs, because the number of traces in the logs is too small, the implicit relationship between activities cannot be represented, resulting in incorrect mining results. The present invention mines logs that contain fewer traces. These traces may be incomplete, but they are effective enough. These traces are used to discover potential relationships, and then according ...

AI Technical Summary

Problems solved by technology

This algorithm can mine causal complete logs, but it cannot solve causal incomplete logs, such as concurrent complete logs

The adaptability of the algorithm is small

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