scholarly journals Research on Influencing Factors of Export Complexity of Chinese High-tech Industry Based on Big Data Analysis

2021 ◽  
Vol 235 ◽  
pp. 03013
Author(s):  
Junsheng Wang ◽  
Zhong Ziqi ◽  
Haoran Wang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Rapid Development ◽  
Big Data Analysis ◽  
High Tech ◽  
Industrial Agglomeration ◽  
High Tech Industry ◽  
Technical Content ◽  
Big Data Technology

Export trade can measure the economic level of a country, but it can only reflect the amount of exports, but not the quality of exported products and the technical content of exported products. Therefore, domestic and foreign scholars have begun to study the complexity of export technology. The development of big data technology makes it possible to analyze the export complexity using big data analysis technology. With the rapid development of high-tech industries represented by high-end manufacturing, there is more and more research on the export of high-tech industries. Based on the existing research results, this article first introduces the current export profile of high-tech products and explains the concept of export complexity. Then, the flow of big data analysis was sorted out. Finally, this paper theoretically analyzes the influence of industrial agglomeration on industrial export complexity, and uses big data analysis and regression verification. The results show that industrial agglomeration has a significant role in promoting the export complexity of China’s high-tech products.

scholarly journals The Construction and Thinking of Cloud Computing and Big Data Technology in Smart Campus

2019 ◽  
Vol 3 (2) ◽  
pp. 152
Author(s):  
Xianglan Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Information Technologies ◽  
Rapid Development ◽  
Smart Campus ◽  
Huge Data ◽  
New Information ◽  
Big Data Technology

<p>In today's society, the rise of the Internet and rapid development make every day produce a huge amount of data. Therefore, the traditional data processing mode and data storage can not be fully analyzed and mined these data. More and more new information technologies (such as cloud computing, virtualization and big data, etc.) have emerged and been applied, the network has turned from informationization to intelligence, and campus construction has ushered in the stage of smart campus construction.The construction of intelligent campus refers to big data and cloud computing technology, which improves the informatization service quality of colleges and universities by integrating, storing and mining huge data.</p>

Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

2021 ◽  
Vol 105 ◽  
pp. 348-355
Author(s):  
Hou Xiang Liu ◽  
Sheng Han Zhou ◽  
Bang Chen ◽  
Chao Fan Wei ◽  
Wen Bing Chang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Teaching Method ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Theoretical Knowledge ◽  
Data Set ◽  
Teaching Mode ◽  
Practice Teaching ◽  
Practical Teaching

The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

scholarly journals An Area-Context-Based Credibility Detection for Big Data in IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Zhao ◽  
Xiang Li ◽  
Jiayue Li ◽  
Jianwen Zou ◽  
Yifan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Detection Method ◽  
Big Data Analysis ◽  
Detection Methods ◽  
High Detection ◽  
Data Credibility ◽  
High Level

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

scholarly journals Big Data Analysis of Water Quality of Secondary Water Supply

2019 ◽  
Vol 154 ◽  
pp. 744-749
Author(s):  
Liu Ying ◽  
Shu Shihu ◽  
Wang Hongyu ◽  
Zhao Xin ◽  
Yan Qi
Keyword(s):  
Water Quality ◽  
Big Data ◽  
Data Analysis ◽  
Water Supply ◽  
Big Data Analysis

scholarly journals Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics

2018 ◽  
Vol 10 (10) ◽  
pp. 3778 ◽  
Author(s):  
Dong-Hui Jin ◽  
Hyun-Jung Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Intelligence ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Sustainable Growth ◽  
Actual Application ◽  
Collection Data ◽  
Information And Communication

Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.

Design of Computer Multimedia Intelligent Platform Using Big Data Analysis

2022 ◽  
Author(s):  
Zhenna Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multimedia Information ◽  
Real Time ◽  
Data Storage ◽  
Big Data Analysis ◽  
Intelligent Vehicle ◽  
Time Data ◽  
Vehicle Operation ◽  
Big Data Technology

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

scholarly journals Big Data Analysis and Research on Fracturing Construction Parameters of Shale Gas Horizontal Wells—A Case Study of Horizontal Wells in Fuling Demonstration Area, China

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8357
Author(s):  
Minxuan Li ◽  
Liang Cheng ◽  
Dehua Liu ◽  
Jiani Hu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Shale Gas ◽  
Optimal Parameter ◽  
Rapid Development ◽  
Horizontal Wells ◽  
Big Data Analysis ◽  
Arithmetic Means ◽  
Correlation Clustering ◽  
Analysis Process

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

Sign in / Sign up

Export Citation Format

Share Document