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>

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

An HBase-Based Platform for Massive Power Data Storage in Power System

2014 ◽  
Vol 1070-1072 ◽  
pp. 739-744
Author(s):  
Zi Jian Yan ◽  
Peng Sun ◽  
Xiao Mei Liu
Keyword(s):  
Data Storage ◽  
Rapid Development ◽  
Database System ◽  
Massive Data ◽  
Huge Data ◽  
Sample Data ◽  
Design Software ◽  
Power Application ◽  
Data Design ◽  
Big Data Technology

With the rapid development of the grid scale, there are huge data generated in grid, which include many sample data, alarm data used for real-time monitor and analysis, the traditional dispatching system can’t meet the demands of big capacity storage very well. In recent years, big data technology develop very fast, among it the distributed column-oriented database system is rising gradually with the vigorous development of cloud computing. Considering the character of power data, this paper studies how to use distributed column-oriented database system in power application for storing massive increasing data. The paper design massive data storage structure of power data, design software frame and deployment of massive power data storage. Through experiment it is feasible for distributed column-oriented database to be applied in EMS system through experiment.

scholarly journals BI warehousing system based on big data

2021 ◽  
Vol 257 ◽  
pp. 02015
Author(s):  
Xiangming Lin ◽  
Kai Liu ◽  
Yixuan Li
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Reference Value ◽  
Data Management System ◽  
System Construction ◽  
Technology Application ◽  
Detailed Explanation ◽  
Warehousing Systems ◽  
Big Data Technology

In the wave of informatization, the data generated by enterprise operations has increased rapidly, prompting the intelligent development of enterprise warehousing systems. In the development of BI warehousing systems, the application of big data technology can promote the rapid development of business intelligence warehousing systems. The application of big data technology in the BI warehousing system can improve the service quality of the data intelligence of the warehousing system. Based on data, it provides support for corresponding decision-making, thereby improving the enterprise data management system. Therefore, this article mainly conducts research and analysis on the construction of BI warehousing system under the application of big data technology, and aims to provide a certain reference value for similar events in the future through a detailed explanation of the current situation of BI warehousing system construction and big data technology application.

Big Data Security Framework for Distributed Cloud Data Centers

2017 ◽  
pp. 288-310 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

Web Services ◽  
2019 ◽  
pp. 240-257
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

2018 ◽  
pp. 589-607 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

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.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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