Analysis of spatial temporal feature of citizen’s wellness behavior based on multi-source big data at the Greater Nanchang Metropolitan Area

Author(s):  
HOU Zhongyan ◽  
LAN Xiaoji
CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xiaozhe Wu ◽  
Kai Zhang ◽  
Jinping Guan ◽  
Bokui Chen ◽  
Yi Zhang ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guojun Ji ◽  
Muhong Yu ◽  
Kim Hua Tan

With the rapid change in technology, cooperative innovation based on data sharing has become an imminent tactic for enterprises to gain competitive advantages. This paper adopted a mixed method approach (case study-modelling-case study) to study firms’ co-opetition behavior based on their data analytics capabilities for innovation. We show that firms favor cooperation among peers with same capabilities, i.e., when each firm’s data level is comparable to their partners. We further establish that data transferability and incentive have high impact on cooperation decisions. Finally, we explain the evolution path of firms’ cooperation decisions and discuss the best options for them to sustain long-term growth and competitiveness. The results provide a basis for firms to decide how best to utilize big data for collaborative innovation, so as to improve customers’ product adoption and reduce costs.


Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.


2019 ◽  
Vol 32 (11) ◽  
pp. 6481-6489
Author(s):  
Hui Hu ◽  
Guofeng Zhang ◽  
Wanlin Gao ◽  
Minjuan Wang

2020 ◽  
Vol 412 ◽  
pp. 339-350
Author(s):  
Linjiang Zheng ◽  
Jie Yang ◽  
Li Chen ◽  
Dihua Sun ◽  
Weining Liu

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