Process Data Analysis Using Visual Analytics and Process Mining Techniques

被引:4
作者
Sitova, Irina [1 ]
Pecerska, Jelena [1 ]
机构
[1] Riga Tech Univ, Dept Modelling & Simulat, Riga, Latvia
来源
2020 61ST INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT SCIENCE OF RIGA TECHNICAL UNIVERSITY (ITMS) | 2020年
关键词
process mining; visual analytics; data visualization; process visualization; production process analysis;
D O I
10.1109/itms51158.2020.9259296
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The research is carried out in the area of production process data analysis. The aim of this research is to explore the applicability of production process visualization methods using process mining techniques for production process analysis. The process mining is considered as a technique for process data analysis from event logs. Event logs store actual information about the course of investigated processes. In order to use this information in the most appropriate way for decision-making, raw event log data is transformed into a suitable format. One of these formats is visual representation. The transformation of data from event logs into informative visualizations is under consideration by approaches of visual analytics. The paper presents methods for analysing data on various levels, such as case, activity, and resource levels. Data is analysed from performance and bottleneck identification perspectives and visualised as area, column, and line graphs. The visualisation type is chosen to best fit the process analysis goals.
引用
收藏
页数:6
相关论文
共 11 条
[1]  
Dumas Marlon, 2018, FUNDAMENTALS BUSINES, DOI DOI 10.1007/978-3-642-33143-5.PDF
[2]  
Fisher B. D, 2005, ILLUMINATING PATH RE
[3]  
Forrest W., 2003, IMPLEMENTING 6 SIGMA, VSecond
[4]  
Kirk A., 2016, Data Visualisation: A Handbook for Data Driven Design
[5]  
Levy D., 2014, DATASET
[6]  
Moreira J., 2019, A General Introduction to Data Analytics
[7]  
Nakatumba J, 2010, LECT NOTES BUS INF P, V43, P69
[8]  
Sitova I., 2019, INTEGRATION PROCESS
[9]  
Van Der Aalst W., 2016, PROCESS MINING DATA
[10]  
van der Aalst WMP, 2011, PROCESS MINING: DISCOVERY, CONFORMANCE AND ENHANCEMENT OF BUSINESS PROCESSES, P1, DOI 10.1007/978-3-642-19345-3