ANALYSIS OF BIG DATA FOR ESTIMATING THE INFORMATIVENESS OF THE COEFFICIENTS OF THE MULTI-TEMPORAL SOIL LINE N-DIMENSIONAL SPACE

2018 ◽  
Author(s):  
Alexey Rukhovich
Keyword(s):  
Big Data ◽  
Dimensional Space ◽  
Soil Line ◽  
Multi Temporal

Parallel kNN Queries for Big Data Based on Voronoi Diagram Using MapReduce

2015 ◽  
pp. 392-414
Author(s):  
Wei Yan
Keyword(s):  
Big Data ◽  
Voronoi Diagram ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
Dimensional Space ◽  
Data Sets ◽  
Two Dimensional ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

In cloud computing environments parallel kNN queries for big data is an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operator widely adopted by many applications including knowledge discovery, data mining, and spatial databases. This chapter proposes a parallel method of kNN queries for big data using MapReduce programming model. Firstly, this chapter proposes an approximate algorithm that is based on mapping multi-dimensional data sets into two-dimensional data sets, and transforming kNN queries into a sequence of two-dimensional point searches. Then, in two-dimensional space this chapter proposes a partitioning method using Voronoi diagram, which incorporates the Voronoi diagram into R-tree. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on R-tree using MapReduce programming model. Finally, this chapter presents the results of extensive experimental evaluations which indicate efficiency of the proposed approach.

Multi-Temporal Insar and Target Detection with COSMO-SkyMed SAR Big Data to Monitor Urban Dynamics in Wuhan (China)

2021 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna ◽  
Timo Balz ◽  
Hashir Tanveer ◽  
Jinghui Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Target Detection ◽  
Urban Dynamics ◽  
Multi Temporal

Characterization of Soil Types and Subtypes in N-Dimensional Space of Multitemporal (Empirical) Soil Line

2018 ◽  
Vol 51 (9) ◽  
pp. 1021-1033
Author(s):  
P. V. Koroleva ◽  
D. I. Rukhovich ◽  
A. D. Rukhovich ◽  
D. D. Rukhovich ◽  
A. L. Kulyanitsa ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Soil Types ◽  
Soil Line

scholarly journals CLUSTERING OF MULTI-TEMPORAL FULLY POLARIMETRIC L-BAND SAR DATA FOR AGRICULTURAL LAND COVER MAPPING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 701-705
Author(s):  
H. Tamiminia ◽  
S. Homayouni ◽  
A. Safari
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Dimensional Space ◽  
Feature Space ◽  
Kernel Functions ◽  
Complex Nature ◽  
Land Cover Mapping ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

HDAC High-Dimensional Data Aggregation Control Algorithm for Big Data in Wireless Sensor Networks

2017 ◽  
Vol 12 (4) ◽  
pp. 72-86 ◽  
Author(s):  
Zeyu Sun ◽  
Xiaohui Ji
Keyword(s):  
Big Data ◽  
Data Aggregation ◽  
Control Algorithm ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Significant Difference ◽  
Low Dimensional ◽  
Aggregation Control

The process of high-dimensional data is a hot research area in data mining technology. Due to sparsity of the high-dimensional data, there is significant difference between the high-dimensional space and the low-dimensional space, especially in terms of the data process. Many sophisticated algorithms of low-dimensional space cannot achieve the expected effect, even cannot be used in the high-dimensional space. Thus, this paper proposes a High-dimensional Data Aggregation Control Algorithm for Big Data (HDAC). The algorithm uses information to eliminate the dimension not matching with the specified requirements. Then it uses the principal components method to analyze the rest dimension. Thus, the simplest method is used to reduce the calculation of dimensionality reduction as can as it possible. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. Finally, the simulation shows that the algorithm can improve the performance of node energy-consumption, rate of the data post-back and the data delay.

Parallel kNN Queries for Big Data Based on Voronoi Diagram Using MapReduce

2016 ◽  
pp. 644-665
Author(s):  
Wei Yan
Keyword(s):  
Big Data ◽  
Voronoi Diagram ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
Dimensional Space ◽  
Data Sets ◽  
Two Dimensional ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

In cloud computing environments parallel kNN queries for big data is an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operator widely adopted by many applications including knowledge discovery, data mining, and spatial databases. This chapter proposes a parallel method of kNN queries for big data using MapReduce programming model. Firstly, this chapter proposes an approximate algorithm that is based on mapping multi-dimensional data sets into two-dimensional data sets, and transforming kNN queries into a sequence of two-dimensional point searches. Then, in two-dimensional space this chapter proposes a partitioning method using Voronoi diagram, which incorporates the Voronoi diagram into R-tree. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on R-tree using MapReduce programming model. Finally, this chapter presents the results of extensive experimental evaluations which indicate efficiency of the proposed approach.

HDAC High-Dimensional Data Aggregation Control Algorithm for Big Data in Wireless Sensor Networks

2020 ◽  
pp. 286-300
Author(s):  
Zeyu Sun ◽  
Xiaohui Ji
Keyword(s):  
Big Data ◽  
Data Aggregation ◽  
Control Algorithm ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Components Method ◽  
Low Dimensional ◽  
Aggregation Control

The process of high-dimensional data is a hot research area in data mining technology. Due to sparsity of the high-dimensional data, there is significant difference between the high-dimensional space and the low-dimensional space, especially in terms of the data process. Many sophisticated algorithms of low-dimensional space cannot achieve the expected effect, even cannot be used in the high-dimensional space. Thus, this paper proposes a High-dimensional Data Aggregation Control Algorithm for Big Data (HDAC). The algorithm uses information to eliminate the dimension not matching with the specified requirements. Then it uses the principal components method to analyze the rest dimension. Thus, the simplest method is used to reduce the calculation of dimensionality reduction as can as it possible. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. Finally, the simulation shows that the algorithm can improve the performance of node energy-consumption, rate of the data post-back and the data delay.

scholarly journals BIG DATA PROCESSING AND ANALYSIS USING MULTI-TEMPORAL SENTINEL 1 DATA TO MANAGE FLOODS IN NORTH OF MOROCCO

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 359-363
Author(s):  
M. Lahsaini ◽  
H. Tabyaoui ◽  
F. El Hammichi
Keyword(s):  
Big Data ◽  
Synthetic Aperture Radar ◽  
Western Mediterranean ◽  
Synthetic Aperture ◽  
Flood Risk Assessment ◽  
Imaging Capability ◽  
Radar Images ◽  
Multi Temporal ◽  
Almost All ◽  
Aperture Radar

Abstract. Floods are the natural hazards that produce the highest number of casualties and material damage in the Western Mediterranean, especially in Morocco. An improvement in flood risk assessment and study of a possible increase in flooding occurrence are therefore needed. Earth Observation big data such as the ones acquired by the Copernicus programme are providing unprecedented opportunities to detect changes and assess economic impacts in case of disasters. This article present the different results obtained by the multi-temporal methods using the Synthetic Aperture Radar images. The spaceborne Synthetic Aperture Radar (SAR) systems are suitable tools for flood mapping thanks to their daytime and nighttime and almost all-weather imaging capability, in addition to their sensitivity to surface roughness and to Flood monitoring. The method has been developed to exploit Sentinel-1 data. It has been tested for the 2018 flood of Tetouan (Morocco).

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