scholarly journals Research on real-time network data mining technology for big data

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jing Hu ◽  
Xianbin Xu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Real Time ◽  
Network Data ◽  
Mining Technology

Early Warning of Power Plant Equipment Based on Massive Real-Time Data Mining Technology

2014 ◽  
Vol 599-601 ◽  
pp. 1487-1490 ◽  
Author(s):  
Li Kun Zheng ◽  
Kun Feng ◽  
Xiao Qing Xiao ◽  
Wei Qiao Song
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Early Warning ◽  
Time Data ◽  
Mining Technology ◽  
Equipment Safety ◽  
State Evaluation ◽  
Power Plant Equipment ◽  
Real Time Data

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.

Big Data Deep Analytics for Geosocial Networks

2018 ◽  
pp. 120-140
Author(s):  
Muhammad Mazhar Ullah Rathore ◽  
Awais Ahmad ◽  
Anand Paul
Keyword(s):  
Big Data ◽  
Real Time ◽  
High Speed ◽  
Heterogeneous Data ◽  
Network Data ◽  
Social Network Data ◽  
Current Trends ◽  
The Government ◽  
Geosocial Networks

Geosocial network data provides the full information on current trends in human, their behaviors, their living style, the incidents and events, the disasters, current medical infection, and much more with respect to locations. Hence, the current geosocial media can work as a data asset for facilitating the national and the government itself by analyzing the geosocial data at real-time. However, there are millions of geosocial network users, who generates terabytes of heterogeneous data with a variety of information every day with high-speed, termed as Big Data. Analyzing such big amount of data and making real-time decisions is an inspiring task. Therefore, this book chapter discusses the exploration of geosocial networks. A system architecture is discussed and implemented in a real-time environment in order to process the abundant amount of various social network data to monitor the earth events, incidents, medical diseases, user trends and thoughts to make future real-time decisions as well as future planning.

Sensor Field Resource Management for Sensor Network Data Mining

2010 ◽  
pp. 280-304
Author(s):  
David J. Yates ◽  
Jennifer Xu
Keyword(s):  
Data Mining ◽  
Response Time ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Resource Consumption ◽  
Data Accuracy ◽  
Network Data ◽  
Sensor Field ◽  
User Queries

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques.

Big data based research on the management system framework of ideological and political education in colleges and universities

2020 ◽  
pp. 1-10
Author(s):  
Yuejun Xia
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Ideological And Political Education ◽  
Political Education ◽  
Representative Point ◽  
Point Sets ◽  
Mining Technology ◽  
Education Management ◽  
Intelligence System

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

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