Simplified Business Process Model Mining Based on Structuredness Metric

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{WeiDongZhao:2011:CIS,

• author = "WeiDong Zhao and Xi Liu and Anhua Wang",
• title = "Simplified Business Process Model Mining Based on Structuredness Metric",
• booktitle = "Seventh International Conference on Computational Intelligence and Security, CIS 2011",
• year = "2011",
• month = "3-4 " # dec,
• pages = "1362--1366",
• address = "Hainan, china",
• keywords = "genetic algorithms, genetic programming, control flow, event logs, fitness function, process complexity metric, process model acquisition, simplified business process model mining, structuredness metric, tree-based individual representation, business data processing, data mining, trees (mathematics)",
• isbn13 = "978-1-4577-2008-6",
• DOI = "doi:10.1109/CIS.2011.303",
• size = "5 pages",
• abstract = "Process mining is the automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them focus on mining models from the prospective of control flow while ignoring the structure of mined models. This directly impacts the understandability and quality of mined models. To address the problem, we have proposed a genetic programming (GP) approach to mining simplified process models. Herein, genetic programming is applied to simplify the complex structure of process models using a tree-based individual representation. In addition, the fitness function derived from process complexity metric provides a guideline for discovering low complexity process models. Finally, initial experiments are performed to evaluate the effectiveness of the method.",
• notes = "Also known as \cite{6128344}",

}

Genetic Programming entries for WeiDong Zhao Xi Liu Anhua Wang

Citations