Data Mining Methods for Analysis and Forecast of an Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers

被引:3
|
作者
Viet, Nguyen Thanh [1 ,2 ]
Kravets, Alla [1 ,3 ]
Hoang, Tu Duong Quoc [1 ]
机构
[1] Volgograd State Tech Univ, Volgograd, Russia
[2] Pham Van Dong Univ, Quang Ngai, Vietnam
[3] Dubna State Univ, Dubna, Russia
来源
关键词
Data mining; Emerging technology; Technology forecast; Technology analysis; Systematic mapping study; SCOPUS; Patent clustering; DISRUPTIVE TECHNOLOGIES; BIBLIOMETRIC ANALYSIS; IDENTIFICATION; FUTURE; VACANT; MODEL; STRATEGY; IDENTIFY; MOBILITY; PATENTS;
D O I
10.1007/978-3-030-86855-0_7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To stay competitive in an environment of rapidly changing science, it is important to monitor the development of existing technology and to discover new and promising technologies. Similarly, it is necessary for a firm to establish a technology development strategy through emerging technology forecast to gain a competitive edge while utilizing limited resources. Numerous methods of emerging technology trend analysis and forecast (TTAF) have been proposed; however, no study described data mining methods' review of this research area in a systematic and structured procedure. Hence, this paper intends to give a review of TTAF data mining methods and shortages by surveying and constructing challenging problems, research and resolving approaches. Moreover, the study highlights adopted data mining methods and types of data sources. Specifically, 50 documents from SCOPUS over a ten-year timespan between 2010 and 2019 were systematically reviewed, and each performing step was followed properly in accordance with systematic mapping study.
引用
收藏
页码:81 / 101
页数:21
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