MOEA for discovering Pareto-optimal process models: an experimental comparison

被引：8

作者：

Deshmukh, Sonia ^{[1
]}

Agarwal, Manoj ^{[2
]}

Gupta, Shikha ^{[3
]}

Kumar, Naveen ^{[1
]}

机构：

[1] Univ Delhi, Dept Comp Sci, Delhi, India

[2] Univ Delhi, Hans Raj Coll, Delhi, India

[3] Univ Delhi, Shaheed Sukhdev Coll Business Studies, Delhi, India

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING | 2020年 / 21卷 / 03期

关键词：

process discovery; evolutionary algorithms; Pareto-front; multi-objective optimisation; process model quality dimensions; PAES; SPEA-II; NSGA-II; completeness; generalisation; OPTIMIZATION; ALGORITHM; EVOLUTION; TASKS;

D O I：

10.1504/IJCSE.2020.106067

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Process mining aims at discovering the workflow of a process from the event logs that provide insights into organisational processes for improving these processes and their support systems. Process mining abstracts the complex real-life datasets into a well-structured form known as a process model. In an ideal scenario, a process mining algorithm should produce a model that is simple, precise, general and fits the available logs. A conventional process mining algorithm typically generates a single process model that may not describe the recorded behaviour effectively. Multi-objective evolutionary algorithms (MOEA) for process mining optimise two or more objectives to generate several competing process models from the event logs. Subsequently, a user can choose a model based on his/her preference. In this paper, we have experimentally compared the popular second-generation MOEA algorithms for process mining.

引用

页码：446 / 456

页数：11

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