Campus Network Management Scheme of Wireless Network User Data Mining

2016 ◽  
Vol 14 (2A) ◽  
pp. 274
Author(s):  
Tang Xiao-kang ◽  
JeongNyeo Kim
Keyword(s):  
Data Mining ◽  
Network Management ◽  
Wireless Network ◽  
Campus Network ◽  
Management Scheme ◽  
User Data

Network Management In A Large-scale OSI-based Campus Network

2005 ◽  
Author(s):  
G. Mansfield ◽  
M. Murata ◽  
K. Higuchi ◽  
K.J. Yanthi ◽  
B. Chakraborty ◽  
...  
Keyword(s):  
Network Management ◽  
Large Scale ◽  
Campus Network

Network Management Scheme Based on TCP/IP Protocol

2013 ◽  
Vol 7 (6) ◽  
pp. 937-945
Author(s):  
Wu Boqiao ◽  
Liu Xuefei ◽  
Tan Aiping ◽  
Weniee Evelyn
Keyword(s):  
Network Management ◽  
Management Scheme

Mobile User Data Mining and Its Applications

2011 ◽  
pp. 216-232
Author(s):  
J. Goh
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

scholarly journals Anomaly data management and big data analytics: an application on disability datasets

2018 ◽  
Vol 2 (2) ◽  
pp. 164-176
Author(s):  
Zhiwen Pan ◽  
Wen Ji ◽  
Yiqiang Chen ◽  
Lianjun Dai ◽  
Jun Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Big Data Analytics ◽  
Quality Data ◽  
Content Type ◽  
Data Mining Algorithms ◽  
Management Scheme ◽  
The Disabled ◽  
Mining Scheme

Purpose The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly. Design/methodology/approach In this paper, the authors proposed a big data management and analytic approach for disability datasets. Findings By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach. Originality/value The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

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