A systematic mapping study of process mining

被引:37
作者
Cardenas Maita, Ana Rocio [1 ]
Martins, Lucas Correa [1 ]
Lopez Paz, Carlos Ramon [2 ]
Rafferty, Laura [3 ]
Hung, Patrick C. K. [3 ]
Peres, Sarajane Marques [1 ]
Fantinato, Marcelo [1 ]
机构
[1] Univ Sao Paulo, Sch Arts Sci & Humanities, Sao Paulo, Brazil
[2] Jose Antonio Echeverra Higher Polytech Inst, Fac Comp Engn, Havana, Cuba
[3] Univ Ontario Inst Technol, Fac Business & Informat Technol, Oshawa, ON, Canada
基金
巴西圣保罗研究基金会;
关键词
Data mining; machine learning; computational intelligence; business process management; process mining; systematic review; BUSINESS PROCESS MANAGEMENT; DISCOVERY; MODELS;
D O I
10.1080/17517575.2017.1402371
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced discovery' (71%) as the main type of process mining addressed and categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are evolutionary computation' (9%) and decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.
引用
收藏
页码:505 / 549
页数:45
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