scholarly journals Spatial data mining on remote sensing perspective

2016 ◽  
Vol 7 (4) ◽  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining

scholarly journals An Empirical Research on Spatial Data Mining

2019 ◽  
Vol 8 (12S2) ◽  
pp. 797-800
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Social Networks ◽  
Knowledge Discovery ◽  
Empirical Research ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Information Discovery ◽  
Promising Area

Spatial data mining is a process of extracting expertise from large volumes of spatial data collected from different applications such as remote sensing, geographic systems and social networks, etc. The collected spatial data are too difficult for the human to analyze. Recent research focuses on data mining to extend the data mining scope from relational storages to spatial databases. A lot of effort put forth to summarize various spatial based knowledge discovery in data mining such as based on generalization, clustering based, spatial associations based, and approximations and aggregations based knowledge discovery are discussed. The discussion shows that spatial data mining is a promising area of information discovery and can lead to extensive research and many challenging issues.

scholarly journals Utilizing MapReduce to Improve Probe-Car Track Data Mining

2018 ◽  
Vol 7 (7) ◽  
pp. 287 ◽  
Author(s):  
Li Zheng ◽  
Meng Sun ◽  
Yuejun Luo ◽  
Xiangbo Song ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Traffic Safety ◽  
Spatial Data ◽  
Road Traffic ◽  
Spatial Data Mining ◽  
Tracking Data ◽  
Mining System ◽  
Computational Performance ◽  
True Path ◽  
Probe Car

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

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