Event interval analysis: Why do processes take time?

被引:23
|
作者
Suriadi, Suriadi [1 ,3 ]
Ouyang, Chun [1 ]
van der Aalst, Wil M. P. [1 ,2 ]
ter Hofstede, Arthur H. M. [1 ,2 ]
机构
[1] Queensland Univ Technol, Brisbane, Qld 4001, Australia
[2] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands
[3] Massey Univ, Albany, New Zealand
基金
澳大利亚研究理事会;
关键词
Process mining; ProM; Data mining; Business process management; MODELS;
D O I
10.1016/j.dss.2015.07.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Through the application of process mining, valuable evidence-based insights can be obtained about business processes in organisations. As a result, the field has seen an increased uptake in recent years as evidenced by success stories and increased tool support. However, despite this impact, current performance analysis capabilities remain somewhat limited in the context of information-poor event logs. For example, natural daily and weekly patterns are not considered but they are vital for understanding the performance of processes and resources. In this paper, a new framework for analysing event logs is defined. Our framework is based on the concept of event interval. The framework allows for a systematic approach to sophisticated performance-related analysis beyond the capabilities of existing log-based analysis techniques, even with information-poor event logs. The paper formalises a range of event interval types and then presents an implementation as well as an evaluation of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:77 / 98
页数:22
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