Paper Info
Title
Towards Discovering Emerging Technologies Based on Decision Tree.
Abstract
It becomes important to discover technical opportunity when we due to uncertainties about forecast. As the internet grows rapidly and the amount of information in the web increases exponentially, however, analysis and forecast regarding the science and technology become more difficult. Because decision of emerging technology needs much cost and time, we need more effective method and solution for decision making of prospective science technologies. For overcoming the above limitations, many methods based on non-systemic processes such as Delphi and Scenario technique was suggested. However, the solutions based on non-systemic processes can not sure accuracy of results and show inconsistent forecasting about the science technology. Therefore we propose the systematic and scientific model for analyzing science technologies and forecasting the emerging technologies in this papers. We obtain features using existing technology lifecycle model and make decision tree model consisted of extracted features. In order to evaluate this model, we did performance test toward 50 technologies in Gartner's Hype cycle for emerging technologies 2009~2010, and can get accuracy of 84%.
Year
DOI
Venue
2011
10.1109/iThings/CPSCom.2011.91
iThings/CPSCom
Keywords
Field
DocType
scenario technique,hype cycle,prospective science technology,science technology,inconsistent forecasting,scientific model,non-systemic process,existing technology lifecycle model,sure accuracy,towards discovering emerging technologies,decision tree,decision tree model,technological forecasting,emerging technology,accuracy,feature extraction,forecasting,decision trees,science and technology
Data science,Technology forecasting,Decision tree,Computer science,Delphi,Decision tree model,Scientific modelling,Emerging technologies,Hype cycle,The Internet
Conference
Citations 
PageRank 
References 
1
0.80
0
Authors
6
Name
Order
Citations
PageRank
Jinhee Lee18021.11
JinHyung Kim221736.55
Seungwoo Lee336642.33
Dongmin Seo44910.64
Hanmin Jung529355.28
Won-Kyung Sung614519.93