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 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 Innovation Path of Leasing Trade under Smart Logistics Technology by Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanan Song ◽  
Xiaolong Hua
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electric Vehicles ◽  
Big Data Analysis ◽  
Experimental Results ◽  
Utilization Rate ◽  
The Internet ◽  
Modern Logistics ◽  
Productivity Level ◽  
Smart Logistics

With the continuous development of big data and the increasing maturity of e-commerce, people’s requirements for modern logistics are becoming increasingly diversified. Conventional modern logistics service level is insufficient to meet the needs of consumers. Therefore, research on the innovative path of big data analysis of leasing trade under smart logistics technology is increasingly important. Smart logistics pays much attention to the integration of the Internet of Things, sensor networks, and the existing internet and realizes the automation, visualization, controllability, intelligence, and networking of logistics through sophisticated, dynamic, and scientific management, thereby improving resource utilization rate and productivity level, creating a more comprehensive connotation of social value. This work aimed to study the use of smart logistics technology to make electric car leasing to a higher level and to innovate the promotion and use mode of leasing trade. A shared business model of electric vehicles was proposed based on the internet and smart logistics technology. The experimental results showed that smart logistics technology contributes to exploring and expanding the role of electric vehicles in solving group users’ short-distance travel and improving an urban transportation system. It could offer a basis for nationwide promotion, thus promoting the development of the new energy vehicle industry and testing the practicability of the model. Regarding the improvement of the sample information system, the experimental results showed that the user’s full satisfaction is approximately 50%, while there remain the customers who would ask for further improvements. Besides, most customers believe that the accuracy and sensitivity of the existing in-vehicle information display need to be enhanced.

Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique

2020 ◽  
pp. 1547-1558
Author(s):  
Kaiyan Han ◽  
Qin Wang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Joint Distribution ◽  
Big Data Analysis ◽  
Experimental Results ◽  
Practical Significance ◽  
Hidden Variable ◽  
Optimal Value ◽  
Sports Venues

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

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
Sign in / Sign up

Export Citation Format

Share Document