scholarly journals Research on Spatial Data Mining in E-Government Information System

10.5772/50245 ◽  
2012 ◽  
Author(s):  
Bin Li ◽  
Lihong Shi ◽  
Jiping Liu ◽  
Liang Wang
Keyword(s):  
Data Mining ◽  
Information System ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Government Information

scholarly journals Deformation information system for facilitating studies of mining-ground deformations, development, and applications

2014 ◽  
Vol 14 (7) ◽  
pp. 1677-1689 ◽  
Author(s):  
J. Blachowski ◽  
W. Milczarek ◽  
P. Stefaniak
Keyword(s):  
Data Mining ◽  
Information System ◽  
Spatial Data ◽  
Ground Deformation ◽  
Data Extraction ◽  
Three Dimensional ◽  
Horizontal Displacement ◽  
Spatial Data Mining ◽  
Calculation Of Parameters ◽  
Ground Deformations

Abstract. The paper presents the concept of the deformation information system (DIS) to support and facilitate studies of mining-ground deformations. The proposed modular structure of the system includes data collection and data visualisation components, as well as spatial data mining, modelling and classification modules. In addition, the system integrates interactive three-dimensional models of the mines and local geology. The system is used to calculate various parameters characterising ground deformation in space and time, i.e. vertical and horizontal displacement fields, deformation parameters (tilt, curvature, and horizontal strain) and input spatial variables for spatial data classifications. The core of the system in the form of an integrated spatial and attributive database has been described. The development stages and the functionality of the particular components have been presented and example analyses utilising the spatial data mining and modelling functions have been shown. These include, among other things, continuous vertical and horizontal displacement field interpolations, calculation of parameters characterising mining-ground deformations, mining-ground category classifications, data extraction procedures and data preparation preprocessing procedures for analyses in external applications. The DIS has been developed for the Walbrzych coal mines area in SW Poland where long-time mining activity ended at the end of the 20th century and surface monitoring is necessary to study the present-day condition of the former mining grounds.

scholarly journals Deformation information system for facilitating studies of mining ground deformations – development and applications

2013 ◽  
Vol 1 (5) ◽  
pp. 4801-4831
Author(s):  
J. B. Blachowski ◽  
W. Milczarek ◽  
P. Stefaniak
Keyword(s):  
Data Mining ◽  
Information System ◽  
Spatial Data ◽  
Ground Deformation ◽  
Data Extraction ◽  
Three Dimensional ◽  
Horizontal Displacement ◽  
Spatial Data Mining ◽  
Displacement Fields ◽  
Ground Deformations

Abstract. The paper presents the concept of the Deformation Information System (DIS) to support and facilitate studies of mining ground deformations. The proposed modular structure of the system includes data collection and data visualisation components, as well as spatial data mining, modelling and classification modules. In addition, the system integrates interactive three-dimensional models of the mines and local geology. The system is used to calculate various parameters characterising ground deformation in space and time, i.e. vertical and horizontal displacement fields, deformation parameters (tilt, curvature and horizontal strain) and input spatial variables for spatial data classifications. The core of the system in the form of an integrated spatial and attributive database has been described. The development stages and the functionality of the particular components have been presented and example analyses utilising the spatial data mining and modelling functions have been shown. These include, among other things, continuous vertical and horizontal displacement fields interpolations, calculation of parameters characterising mining ground deformations, mining ground category classifications, data extraction procedures and data preparation, pre-processing procedures for analyses in external applications. The DIS has been developed for the Walbrzych Coal Mines area in SW Poland where long-time mining activity has finished at the end of the 20th Century and surface monitoring is necessary to study present day condition of the former mining grounds.

scholarly journals Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Arvind Sharma ◽  
R. K. Gupta ◽  
Akhilesh Tiwari
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Objects

There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise). The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

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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