Research on Online Learning Platform Based on Cloud Computing and Big Data Technology

2020 ◽  
pp. 359-366
Author(s):  
Guan-Qun Cai ◽  
Qing-Hua Wang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Online Learning ◽  
Learning Platform ◽  
Big Data Technology

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>

scholarly journals Big data technology in construction

2019 ◽  
Vol 97 ◽  
pp. 01032 ◽  
Author(s):  
Nikolay Garyaev ◽  
Venera Garyaeva
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Life Cycle ◽  
Construction Industry ◽  
Smart Cities ◽  
Entire Life ◽  
Entire Life Cycle ◽  
Intelligent Buildings ◽  
Big Data Technology

The article presents the results of the analysis of the use of large amounts of data in the construction industry, new trends such as BIM, IOT, cloud computing, intelligent buildings and smart cities with great prospects for application. These problems are related to the presence of huge amounts of data produced by the construction industry during the entire life cycle of a building, which are not fully used for optimizing processes and making decisions in construction.

Cloud Computing Model for Big Geological Data Processing

2013 ◽  
Vol 475-476 ◽  
pp. 306-311 ◽  
Author(s):  
Miao Miao Song ◽  
Zhe Li ◽  
Bin Zhou ◽  
Chao Ling Li
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Parallel Processing ◽  
Data Processing ◽  
Processing Unit ◽  
Geological Data ◽  
Computing Model ◽  
Operation Speed ◽  
Graphics Processing ◽  
Big Data Technology

Geological data with phyletic and various, huge and complex data format, the analysis of geological data processing is mainly divided into three parts: Mines forecast, mine evaluation and mine positioning. Traditional geological data analysis model is limited by limited storage space and computational efficiency, and cannot meet the needs of a large number of geological data fast operations. "Big data technology" provides the ideal solution to the vast amounts of geological data management, information extraction, and comprehensive analysis. For mass storage capacity and high-speed computing power that the "big data technology" need, we built an intelligence systems applied to the analysis of geological data based on MapReduce and GPU double parallel processing cloud computing model. For a large number of geological data, using hadoop cluster system to solve the problem of large amounts of data storage, and designing efficient parallel processing method based on GPU (Graphics Processing Units: calculation of Graphics Processing unit), the method was applied to MapReduce framework, finally completing MapReduce and GPU double parallel processing cloud computing model to improve the operation speed of the system. Through theoretical modeling and experimental verification, indicating that the system can meet the analysis of geological data operation precision, the operation data amount and the operation speed.

Research on University Accounting Teaching Innovation Based on big data

2021 ◽  
Vol 7 (5) ◽  
pp. 4384-4392
Author(s):  
Hongxuan Ma
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Accounting Education ◽  
Financial Accounting ◽  
Teaching Innovation ◽  
Teaching Reform ◽  
The University ◽  
Education In China ◽  
Education Concept ◽  
Big Data Technology

Objectives: With the arrival of the era of big data, the use of cloud computing technology has the characteristics of large capacity, variety and speed. Big data makes the teaching of financial accounting more convenient and efficient. Methods: It is an effective way to realize the goal of modernization of education in China by paying attention to the development and application of the reform of accounting teaching in Universities under the educational technology of big data. Results: This paper from the transformation of the university accounting education concept, in the era of big data under the background of accounting education in Colleges and universities how to give full play to the advantages of cloud computing, cloud platform construction of accounting education, then explore the reform of accounting education mode of. Conclusion: Therefore, this paper from the perspective of big data technology background, starting from the reform of the teaching mode of accounting and accounting industry background, considering the direction from the accounting personnel training, put forward the corresponding countermeasures for accounting teaching methods and teaching contents, promoting college accounting teaching reform.

Teaching Decision-Making Based on Online Learning Big Data of Tobacco Courses: The Perspective of Student Portraits

2021 ◽  
Vol 7 (6) ◽  
pp. 5413-5426
Author(s):  
Liu Ziyu ◽  
Yao Mengying ◽  
Cao Shugui
Keyword(s):  
College Students ◽  
Decision Making ◽  
Big Data ◽  
Online Learning ◽  
Student Groups ◽  
Learning Platform ◽  
Learning Conditions ◽  
Label System ◽  
Individualized Teaching ◽  
Student Information

The high-quality development and technological upgrading of the tobacco industry put forward higher requirements for the overall quality of talents. In the context of the increasing popularity of blended teaching, in order to help teachers, major in tobacco, tomake better teaching decisions in the teaching process, guide college students majoring in tobacco to better complete their studies and provide timely warnings for students’ unhealthy conditions, this article proposes a method to assist teachers in teaching decision-making based on student portraits constructed based on online learning big data. First, collect basic student information and student learning information from the online learning platform. Secondly, preprocess of the data, delete data and normalize dense data. Then, collect and classify student information to form a portrait of basic student information, a portrait of learning achievements, a portrait of learning active level and a portrait of learning status. Analyze the portrait to guide and assist students in their learning and to give early warning of bad learning conditions. At last, analyze the student portraits according to different rules and put forward corresponding suggestions according to the characteristics of different groups of college students. According to the learning situation of learners majoring in tobacco, the article constructs the student portrait label system and portrait model. According to the constructed student portrait, it puts forward learning suggestions for individual students and student groups respectively. In the field of tobacco teaching, it has certain reference significance and application value in providing decision-making reference for differentiated and individualized teaching and assisting teaching decision-making.

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