Big Data New Frontiers: Mining, Search and Management of Massive Repositories of Solar Image Data and Solar Events

2014 ◽  
pp. 151-158 ◽  
Author(s):  
Juan M. Banda ◽  
Michael A. Schuh ◽  
Rafal A. Angryk ◽  
Karthik Ganesan Pillai ◽  
Patrick McInerney
Keyword(s):  
Big Data ◽  
Image Data ◽  
Solar Image ◽  
Solar Events

scholarly journals A Systematic Analysis of Big Image Data Methodologies in Various Applications

2020 ◽  
Vol 9 (5) ◽  
pp. 483-487
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Scale ◽  
Image Data ◽  
Computational Time ◽  
Process Data ◽  
Systematic Analysis ◽  
Large Scale Data ◽  
Learning Techniques ◽  
Effective Performance

Big data is large-scale data collected for knowledge discovery, it has been widely used in various applications. Big data often has image data from the various applications and requires effective technique to process data. In this paper, survey has been done in the big image data researches to analysis the effective performance of the methods. Deep learning techniques provides the effective performance compared to other methods included wavelet based methods. The deep learning techniques has the problem of requiring more computational time, and this can be overcome by lightweight methods.

Big Image Data Management on Portable Equipment

2013 ◽  
Vol 756-759 ◽  
pp. 905-910 ◽  
Author(s):  
Ye Liang
Keyword(s):  
Big Data ◽  
High Energy Physics ◽  
Image Data ◽  
High Energy ◽  
Geographical Information ◽  
Control Technique ◽  
Portable Equipment ◽  
Data Intensive ◽  
Diverse Field ◽  
Information Image

Big Data is a new label given to a diverse field of data intensive informatics in which the data sets are so large that they become hard to work with effectively. The term has been mainly used in two contexts, firstly as a technological challenge when dealing with data-intensive domains such as geographical information image, high energy physics, astronomy or internet search, and secondly as a sociological problem when data about us is collected and mined by companies such as Facebook, Google, mobile phone companies, retail chains and governments. In this paper we look at this first issue from a new perspective, namely how can the user gain awareness of the personally relevant part big data that is publicly available in the portable equipment. With a lot of traditional applications such as geography information system (GIS) implanted on portable equipment, how to collect, store, process, analyze, and display big image data becomes a hot field. This paper puts forward a display control technique on portable equipment, which is based on measurement of users location. At the same time, we do serials of experiment on Android platform to validate them.

Standardized coordinate systems for solar image data

2002 ◽  
Vol 29 (12) ◽  
pp. 2093-2098 ◽  
Author(s):  
William T. Thompson
Keyword(s):  
Image Data ◽  
Solar Image ◽  
Coordinate Systems

scholarly journals Big Data Analytics for Images in Public Cloud using Map Reduce on Local Clusters

2019 ◽  
Vol 8 (4) ◽  
pp. 7384-7390
Keyword(s):  
Big Data ◽  
Programming Model ◽  
Scale Up ◽  
Research Work ◽  
Big Data Analytics ◽  
Image Data ◽  
Blue Color ◽  
Public Cloud ◽  
File Size ◽  
Local Cluster

MapReduce is a programming model used for parallel computing of big data in public cloud. Big Data have characteristics like variety, velocity and volume. The research work carries out MapReduce using Matlab which is a powerful image processing and numeric computation tool. The research considers unstructured image data in public cloud Dropbox as big data and applies MapReduce algorithm to map and reduce all the images stored in it. The research work aims to retrieve the images in public cloud with maximum Red, Green, Blue color and the colors that intersect between them. The same code is modified to find all Red, Green and Blue that supports more parallelism and aids in improving the speed of MapReduce by eliminating the dependency between iterations. The speed of parallel MapReduce shows considerable improvement only with increased file size and coding style. Parallel MapReduce computation is carried out with default workers, three and four workers of the local cluster with scale up architecture. This model is developed using Matlab and can be implemented in Hadoop as well.

scholarly journals PARSUC: A Parallel Subsampling-Based Method for Clustering Remote Sensing Big Data

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3438 ◽  
Author(s):  
Xia ◽  
Huang ◽  
Li ◽  
Zhou ◽  
Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Image Data ◽  
Data Partitioning ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Hadoop Platform ◽  
Parallel Clustering

Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD.

scholarly journals ROI-based efficient video data processing for large-scale cloud storage in intelligent CCTV environment

2018 ◽  
Vol 7 (3.3) ◽  
pp. 151 ◽  
Author(s):  
Dong Hyeok Lee ◽  
Nam Je. Park
Keyword(s):  
Big Data ◽  
Cloud Storage ◽  
Large Scale ◽  
Image Data ◽  
Processing Technique ◽  
Video Data ◽  
Image Processing Technique ◽  
Efficient Data ◽  
Transmission Structure ◽  
Cloud Storage Environment

Background/Objectives: Big data environment is being realized. Recently, intelligent public safety environment on the foundation of the image processing technique based on big data is being introduced, and accordingly, processing CCTV images is becoming more important day by day.Methods/Statistical analysis: In this paper, an efficient technique to send image information for mass cloud storage environment was proposed. With the offered method, only the ROI area is extracted and partial object images are transmitted, and it has the strengths of higher efficiency and protected privacy with the application of a masking technique.Findings: it is general to apply the masking technique partially to face information, and in this study, the privacy of the image data registered in the cloud storage was to be protected based on this masking technique, and an efficient data transmission structure grounded on ROI area extraction was proposed.Improvements/Applications: With the offered method, only the ROI area is extracted and partial object images are transmitted, and it has the strengths of higher efficiency and protected privacy with the application of a masking technique.  

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