Algorithm Analysis for Big Data in Education Based on Depth Learning

Zhang, Wenjie; Jiang, Liehui

doi:10.1007/s11277-018-5331-3

Cited by 24 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then they point out the challenges. [23] They discuss big data in Nurse Education. They explain the benefit of big data in Nurse Education, challenges, and potential dangers.…”

Section: Related Workmentioning

confidence: 99%

Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.

show abstract

“…Then they point out the challenges. [23] They discuss big data in Nurse Education. They explain the benefit of big data in Nurse Education, challenges, and potential dangers.…”

Section: Related Workmentioning

confidence: 99%

Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…Several technologies have been used to create smart campus solutions. Among them, the following stand out: Big Data [Hu and Yan 2016, Wei 2016; cloud computing Zhang 2015, Zhang andJiang 2018]; Internet of Things (IoT) [Arshad et al 2017, Guo and Zhang 2015, Ivanovski 2015, Majeed and Ali 2018; Internet and high performance computing [Nie 2013]; virtualization, mobile Internet, and social networks [Hu and Yan 2016]; sensors and common communication interfaces [Nati et al 2017]; 6LoWPAN (IPv6 over Low Power Wireless Personal Area Networks) [Luo 2018]; and RFID (Radio-Frequency Identification) [Kwok 2015].…”

Section: Related Workmentioning

confidence: 99%

Towards a Simulator for Green Smart Campus Systems

Moraes¹,

Pisani²,

Borin³

2020

Anais Do Workshop De Computação Aplicada À Gestão Do Meio Ambiente E Recursos Naturais (WCAMA 2020)

View full text Add to dashboard Cite

The 2030 Agenda for Sustainable Development aims to foster environmental protection, inclusive economic growth, and social inclusion. Universities are called to join this global effort and are expected not only to contribute with solutions for the sustainability challenges but also provide sustainable campi. Given this scenario, the main contributions of this paper are threefold: i) to illustrate how universities could contribute with the 2030 Agenda through smart campus solutions based on technologies such as Internet of Things, Big Data, and Cloud Computing; ii) to propose a framework to automatically collect data to generate and manage sustainability indicators in the university; and iii) to propose a simulator for green smart campus system which allows an adequate estimate of the required information and communication infrastructure.

show abstract

“…)Methods included in the Data Mining technology are briefly discussed. It is noted that cluster analysis is especially important for the construction industry, as this method allows to combine construction objects into homogeneous groups and then purposefully investigate these groups [12].…”

Section: Midrise Buildingsmentioning

confidence: 99%

Research of the possibilities of application of the Data Warehouse in the construction area

2018

View full text Add to dashboard Cite

Today, in information technologies, the direction associated with the use of Data Warehouse (DW) is evolving very dynamically. Using DW, it is possible to implement two types of data analysis: OLAP-analysis: a set of technologies for the rapid processing of data presented as a multidimensional cube; Data Mining is an intelligent, deep analysis of data to detect previously unknown, practically useful patterns (in our case, the construction area). It is noted, that of all the methods used in technology Data Mining, cluster analysis is especially useful for the construction area. At present, the role of DW has increased, significantly due to the fact, that many methods and approaches of Data Mining have formed the basis of a new, promising method of Big Data. We will specify that, that Data processing from the Data Warehouse with the help of technology Big Data, allows to deduce researches in a building area to the higher level. The purpose of this work is to research of the possibilities of application of the Data Warehouse in the construction area. The article suggests the new approach to data analysis in the construction area, based on the use of Big Data technology and elements of OLAP - analysis. In the section “Discussion” is considering the possibility of the new promising business in the construction field, based on the application of Data Warehouse and technology Big Data.

show abstract

Algorithm Analysis for Big Data in Education Based on Depth Learning

Cited by 24 publications

References 14 publications

Big Data Application and its Impact on Education

Big Data Application and its Impact on Education

Towards a Simulator for Green Smart Campus Systems

Research of the possibilities of application of the Data Warehouse in the construction area

Contact Info

Product

Resources

About