scholarly journals Research on Information Resource Sharing and Big Data of Sports Industry in the Background of OpenStack Cloud Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chuan Mou ◽  
Ye Cheng
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Data Analysis ◽  
Resource Sharing ◽  
Positive Impact ◽  
Rapid Development ◽  
Information Resources ◽  
Cloud Platform ◽  
Positive Role ◽  
Sports Industry

The rapid development of information technology and Internet makes the sports information resources retrieval service more convenient and quick; sports policy in recent years lays a foundation for the development of the Internet + sports, the development of sports industry in the process of our country economy level of development status, and the development of sports industry into the era of information and big data. This paper takes OpenStack cloud platform as the research basis (1) to realize the sharing of sports industry information resources in OpenStack cloud technology and (2) to realize big data analysis of sports industry and (3) empirical research on big data of sports industry. The main content is to realize the construction of sports resources informatization based on the OpenStack cloud platform. Through the analysis and empirical study of the big data of the sports industry, the influence of the development of the sports industry in the process of China’s economic development is discussed. In this paper, the experimental results show that the sports industry showed a positive impact in the process of economic development, the sports economy for the development of the economy, the contribution rate reached 11.77%, the sports industry for the development of the economy, the pull rate of 1.056%, based on the cloud platform of information resources sharing of data analysis, sports industry for the development of the economy has a positive role in promoting.

scholarly journals Sports Big Data Analysis Based on Cloud Platform and Its Impact on Sports Economy

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ye Cheng ◽  
Yan Song
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Data Analysis ◽  
Rapid Development ◽  
Information Age ◽  
Big Data Analysis ◽  
Internet Technology ◽  
Support Vector ◽  
Cloud Platform ◽  
The Impact

The service information system is constantly transforming to a networked information model, and domestic hardware equipment is constantly updated. Independent controllability has also become the basic requirement of the new information age. With the development of the information age and the new era of independent control, more and more services and applications will also be deployed on autonomous and controllable cloud platforms. With the rapid development of Internet technology in the information age and the resulting changes in productivity, people can record, store, and transmit more and more information. When information becomes recordable, storage, and easy to transmit, information becomes modern meaning nowadays, an era of information explosion characterized by massive, volatile, timely transmission, and diverse forms has truly come, forming what is now called the “big data era”. This article mainly introduces the analysis of sports big data based on the cloud platform and the research on the impact on the sports economy and intends to provide ideas and directions for the analysis of sports big data and the research on the impact on the sports economy. This paper proposes a cloud platform-based sports big data analysis and research methods for its impact on the sports economy, including the use of Hadoop cloud platform big data processing systems and support vector regression algorithms for cloud platform-based sports big data analysis and sports economy. The experimental results of this paper show that the average correlation between sports big data analysis and sports economic development is 0.5155, and appropriate cloud platform-based sports big data analysis plays a positive role in promoting sports economic development.

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 THE ROLE OF BANKS AND SECURITIES MARKETS IN THE POST-TRANSITION ECONOMIES OF EASTERN EUROPE

2020 ◽  
Author(s):  
Sasho Arsov
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Eastern Europe ◽  
Banking Sector ◽  
Positive Impact ◽  
Financial Sector ◽  
Securities Markets ◽  
Positive Role ◽  
The Impact

Economic theory predicts that the development of the financial sector should have a positive impact on the overall economic development. Research has predominantly confirmed this expectation, with the remark that at earlier stages of economic development this impact should be higher, while a disproportionate banking sector has detrimental effect on growth through its impact on attracting highly skilled workforce, increased presence of moral hazard and the associated banking crises. This issue has been studied only occasionally in the case of the former socialist economies of Central and Eastern Europe and the former USSR. This paper represents an attempt to analyze the impact of the banking sector and securities markets development on the economic growth of these countries. A sample of 22 countries is assembled, using data from 1995 to 2018 and a panel regression and a GMM technique are used to derive conclusions on the researched topic. The analysis has shown that the banking sector has played a positive role in the economic growth throughout the analyzed period, while the role of the stock market is not significant. This is in line with the previous studies which have confirmed that the positive role of the securities markets should be expected only at higher levels of economic development. Also, the impact of the overall financial sector is deemed to be positive.

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.

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

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.

Comparison of the Level of Familiarity of IT Units' Staff and students with Big Data Analyzes

2020 ◽  
Author(s):  
Elham Nazari ◽  
Parnian Asgari ◽  
hamed tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Individual Characteristics ◽  
Scientific Study ◽  
Level Of Knowledge ◽  
And Gender ◽  
Analytical Approaches

Abstract introduction The rapid development of technology in recent decades has led to the production of a huge amount of data. This type of data analysis that is called Big Data Analysis obtain Many benefits, including reducing costs. One of the challenges of these analyses is the lack of specialized expertise and knowledge in this area. The purpose of this study was to compare the familiarity of IT staff and students with big data analyzes at various universities and organizations. Materials and method This analytical study was conducted on IT units' staff and students of different organizations and universities in Mashhad, Iran. A questionnaire was designed based on reviewing the texts published in PubMed, google scholar, science direct, and EMBASE databases and using the Delphi method and the attendance of 10 specialists in different disciplines. The designed questionnaire evaluated the participants' knowledge about the Big Data analyzes in two parts. The participants were 265 IT units' staff and students of different organizations, completing the designed questionnaire. Participants' opinion was evaluated using two descriptive and analytical approaches. The relationship between knowledge scores and individual characteristics such as gender, age, work experience, Field of study, degree, the average number of hours’ scientific study and non-scientific study per week was examined. To investigate the synchronous and reciprocal effects GLM was used. Results Scores earned by students and staff were 2.66 ± 1.13 and 2.28 ± 1.21 respectively that p =. 012 represented a significant correlation between the level of knowledge of students and staff. In other words, the level of knowledge of staff about big data was more than the level of knowledge of the students.The correlation of each of the variables was not significant with the score of the Big Data Analysis Knowledge.But There was a significant correlation between experience and gender with the knowledge scores. Conclusions In general, the level of knowledge in analyzing big data in different groups of people was at a low level that implementing measures such as holding training courses in this field seems necessary.

Research on E-Government Information Resources Sharing System Based on Cloud Computing

2014 ◽  
Vol 687-691 ◽  
pp. 1758-1761
Author(s):  
Meng Han
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Resource Sharing ◽  
Rapid Development ◽  
Information Resources ◽  
Network Technology ◽  
Government Information ◽  
Research Focus ◽  
Resources Sharing ◽  
Information Resources Sharing

With the rapid development of network technology, information technology has become a trend. However, our country’s government information technology still needs to improve as quickly as possible. The key to promote the construction of government information is to establish e-government information resource sharing system. Therefore, how to achieve e-government information resources sharing becomes a research focus. In the construction of e-government information resources sharing system, cloud computing playing a role cannot be ignored. In the paper, it will research and analysis how to construct e-government information resources sharing system.

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