Improving Process Discovery Algorithms Using Event Concatenation

Maryam Pishgar

doi:10.1109/ACCESS.2022.3185235

2022-01-01

Maryam Pishgar

Martha Razo

Houshang Darabi *

DOI：10.1109/ACCESS.2022.3185235

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摘要

En 中文

Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm's filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model's F-Measure.

Keyword:

Behavioral sciences

Concurrent computing

Noise measurement

Filtering

Complexity theory

Heuristic algorithms

Data mining

Process mining

concurrent relations

concatenation

pre-processing methods