Cloud-Based Big Data Analytics in Smart Educational System

2018 ◽  
pp. 189-199 ◽  
Author(s):  
Newlin Rajkumar Manokaran ◽  
Venkatesa Kumar Varathan ◽  
Shalinie Deepak
Keyword(s):  
Big Data ◽  
Educational System ◽  
Data Analytics ◽  
Low Cost ◽  
Big Data Analytics ◽  
Digital Data ◽  
Individual Student ◽  
Cloud Environment ◽  
Technology In The Classroom ◽  
Digital Era

In this modern Digital era, Technology is a key player in transforming the educational pedagogy for the benefit of students and society at large. Technology in the classroom allows the teacher to deliver more personalized learning to the student with better interaction through the internet. Humongous amount of digital data collected day by day increases has led to the use of big data. It helps to correlate the performance and learning pattern of individual students by analysing large amount of stored activity of the students, offering worthwhile feedback etc. The use of big data analytics in a cloud environment helps in providing an instant infrastructure with low cost, accessibility, usability etc. This paper presents an innovative means towards providing a smarter educational system in schools. It improves individual efficiency by providing a way to monitor the progress of individual student by maintaining a detailed profile. This framework has been established in a cloud environment which is an online learning system where the usage pattern of individual students are collected.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

scholarly journals Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

scholarly journals Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171882381 ◽  
Author(s):  
Lucy Resnyansky
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Task Demands ◽  
Digital Data ◽  
Conceptual Frameworks ◽  
Social Scientists ◽  
The Social ◽  
Epistemological Challenge ◽  
Social Scientific

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.

Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2022 ◽  
pp. 245-261
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

2018 ◽  
pp. 61-78 ◽  
Author(s):  
Murad Khan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
High Time ◽  
Digital Data ◽  
Learning Mechanisms ◽  
Digital World

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.

Librarian’s perspective for the implementation of big data analytics in libraries on the bases of lean-startup model

2019 ◽  
Vol 36 (1) ◽  
pp. 21-37
Author(s):  
Khurshid Ahmad ◽  
Zheng JianMing ◽  
Muhammad Rafi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Privacy ◽  
Core Competencies ◽  
Big Data Analytics ◽  
Data Availability ◽  
Digital Data ◽  
Data Organization ◽  
Content Type ◽  
Lean Startup

Purpose This study aims to propose a model based on philosophical thoughts of Dr S.R Ranganathan and the lean-startup method for the execution of big data analytics (BDA) in libraries. The research paves a way to understand the role and required competencies of Library and Information Science (LIS) professionals for the implementation of BDA in libraries. Design/methodology/approach In the BDA analytics context, a session with a proposed model was presented to the audience to get the response of librarians about the required competencies and skills. The research tool was developed based on the literature review to know the role of LIS professionals and their required competencies/skills for BDA. The questionnaire was distributed in the BDA session to collect the responses of the participating audience on the variables that focused on the role and core competencies of LIS professionals in BDA. In the analysis of results, the independent t-test was applied to know the mean value of the overall response rate. Findings The findings show that perceptions of LIS professionals in the understanding of BDA ranked high in data privacy, data availability, data organization and data literacy. Digital data curation, policies supervision and providing the data consultancy also showed a significant relationship among these variables. Besides, the correlation between the required skills for BDA, metadata skills, data ethics, data acquisition, data cleaning, data organization, data analysis, digital curation, data clustering, data protection rules and digital visualization also showed a beneficial relationship. Originality/value This study also helps to understand the perspective of LIS professionals for the implementation of BDA in libraries and to fill the literature gap in the respective.

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