scholarly journals Information Visualization from the Perspective of Big Data Analysis and Fusion

2021 ◽  
Vol 20 ◽  
pp. 352-361
Author(s):  
Xiang Lin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Information Visualization ◽  
Information Fusion ◽  
Big Data Analysis ◽  
Data Sources ◽  
Visualization Technique ◽  
Network Layout ◽  
Data Volume ◽  
Data Environment

In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. However, there are several defects with the research on information association of L&I visualization network: the lack of optimization of network layout algorithms, and the absence of L&I information fusion and comparison in multiple disciplines, in the big data environment. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network, and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in details. Experimental results verify the effectiveness of the proposed algorithm

scholarly journals Information Visualization from the Perspective of Big Data Analysis and Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Lin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Information Visualization ◽  
Big Data Analysis ◽  
Experimental Results ◽  
Data Sources ◽  
Visualization Technique ◽  
Data Volume ◽  
Data Environment ◽  
Information Association

In the big data environment, the visualization technique has been increasingly adopted to mine the data on library and information (L&I), with the diversification of data sources and the growth of data volume. The previous research into the information association of L&I visualization network rarely tries to construct such a network or explore the information association of the network. To overcome these defects, this paper explores the visualization of L&I from the perspective of big data analysis and fusion. Firstly, the authors analyzed the topology of the L&I visualization network and calculated the metrics for the construction of L&I visualization topology map. Next, the importance of meta-paths of the L&I visualization network was calculated. Finally, a complex big data L&I visualization network was established, and the associations between information nodes were analyzed in detail. Experimental results verify the effectiveness of the proposed algorithm.

scholarly journals Big Data from A to Z

2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Elham Nazari ◽  
Marziyeh Afkanpour ◽  
Hamed Tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Data Sources ◽  
The Past ◽  
Logical Time ◽  
Definition Of

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.

scholarly journals Astrophysics and Big Data: Challenges, Methods, and Tools

2016 ◽  
Vol 12 (S325) ◽  
pp. 345-348 ◽  
Author(s):  
Mauro Garofalo ◽  
Alessio Botta ◽  
Giorgio Ventre
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Field Research ◽  
Big Data Analysis ◽  
Learning Models ◽  
Huge Amount ◽  
Complex Variety ◽  
Data Volume ◽  
Machine Learning Models

AbstractNowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.

scholarly journals 5G-Oriented IoT Big Data Analysis Method System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Hu ◽  
Xianling Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Internet Of Things ◽  
Big Data Analysis ◽  
System Model ◽  
Analysis Method ◽  
Data Environment ◽  
Internet Of Things Technology ◽  
And Storage

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.

Insights into seismic hazard from big data analysis of ground motion simulations

2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Big Data Analysis ◽  
Ground Motion Simulations
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