Experiment of Solar Semiconductor Condensing Wall Water Intake System Based on Big Data

Abstract With the rapid development of industry and agriculture, fresh water resources are increasingly scarce, desertification control project investment is more, but the benefit is small. In order to solve the practical problem of water scarcity in desert areas and the waste caused by water not being applied precisely to plants, Based on the rapid development of 5G network signal transmission technology and big data analysis technology, a set of solar semiconductor condensation wall water intake system is designed to inject new vitality into the process of desertification control by taking advantage of the environmental and climatic characteristics of desert areas. Through theoretical analysis, big data analysis and experimental study of the system, taking Liangzhou District of Wuwei City in Gansu Province as an example, the comprehensive analysis of the system was carried out. The results show that the theoretical water output per hour of the system is 138.3g, and the cumulative power generation of the system in 20 years is 28800kWh, which can reduce standard coal combustion by 8.64t, reduce CO2222.64t, and save 33609.6 yuan.

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

Advances in Science and Technology ◽

10.4028/www.scientific.net/ast.105.348 ◽

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.

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

Sustainability ◽

10.3390/su10103778 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3778 ◽

Cited By ~ 9

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.

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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 ◽

10.3390/en14248357 ◽

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.

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

10.21203/rs.2.22539/v1 ◽

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.

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Big Data from A to Z

Frontiers in Health Informatics ◽

10.30699/fhi.v8i1.202 ◽

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.

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Frontiers in the emerging development of blockchain and Bitcoin: visual research based on big data analysis

E3S Web of Conferences ◽

10.1051/e3sconf/202123503017 ◽

2021 ◽

Vol 235 ◽

pp. 03017

Author(s):

Chung-Lien Pan ◽

Zizhen Chen ◽

Zhixiang Zhou ◽

Zhuocheng Cai ◽

Xuanyan Liu ◽

...

Keyword(s):

Big Data ◽

Data Analysis ◽

Web Of Science ◽

Rapid Development ◽

Visual Presentation ◽

Big Data Analysis ◽

Research Progress ◽

Visual Research ◽

Key Points ◽

External Development

In the era of the rapid development of information technology, the innovation of Fintech continues to send emerging research hotspots to the financial market. Based on the analysis of documents retrieved from the Web of Science database, this article provides a comprehensive data analysis and visualization of keywords such as “blockchain”, “bitcoin”, and “business and economic”. Using big data analysis technology and visual presentation, the author analyzed the details of the author’s keywords, popular organizations, countries, sources, and other key points of the correlation and external development status. Show the researchers the influence of the intersection of keywords, and point out the leading status of the organizations or countries with large resource occupancy in the research progress; at the same time, provide the researchers with an accurate grasp of the direction of the field and provide a reliable basis.

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

E3S Web of Conferences ◽

10.1051/e3sconf/202123503013 ◽

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.

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

Mathematical Problems in Engineering ◽

10.1155/2021/6610000 ◽

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.

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