Emerging Trends in Cloud Computing, Big Data, Fog Computing, IoT and Smart Living

2017 ◽  
pp. 29-40 ◽  
Author(s):  
Amin Hosseinian-Far ◽  
Muthu Ramachandran ◽  
Charlotte Lilly Slack
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Fog Computing ◽  
Emerging Trends ◽  
Smart Living

scholarly journals Cloud Computing in Singapore: Key Drivers and Recommendations for a Smart Nation

2018 ◽  
Vol 6 (4) ◽  
pp. 39-47 ◽  
Author(s):  
Reuben Ng
Keyword(s):  
Public Policy ◽  
Cloud Computing ◽  
Big Data ◽  
Business Models ◽  
Fog Computing ◽  
Novel Technologies ◽  
Cloud Computing Adoption ◽  
Big Data Applications ◽  
Key Drivers ◽  
The Government

Cloud computing adoption enables big data applications in governance and policy. Singapore’s adoption of cloud computing is propelled by five key drivers: (1) public demand for and satisfaction with e-government services; (2) focus on whole-of-government policies and practices; (3) restructuring of technology agencies to integrate strategy and implementation; (4) building the Smart Nation Platform; (5) purpose-driven cloud applications especially in healthcare. This commentary also provides recommendations to propel big data applications in public policy and management: (a) technologically, embrace cloud analytics, and explore “fog computing”—an emerging technology that enables on-site data sense-making before transmission to the cloud; (b) promote regulatory sandboxes to experiment with policies that proactively manage novel technologies and business models that may radically change society; (c) on the collaboration front, establish unconventional partnerships to co-innovate on challenges like the skills-gap—an example is the unprecedented partnership led by the Lee Kuan Yew School of Public Policy with the government, private sector and unions.

Emerging Trends of Big Data in Cloud Computing

2021 ◽  
pp. 38-55
Author(s):  
Poonam Nandal ◽  
Deepa Bura ◽  
Meeta Singh
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Software Framework ◽  
Effective Solution ◽  
Apache Hadoop ◽  
Large Sets ◽  
Exponential Increase ◽  
Emerging Trends

In today's world where data is accumulating at an ever-increasing rate, processing of this big data was a necessity rather than a need. This required some tools for processing as well as analysis of the data that could be achieved to obtain some meaningful result or outcome out of it. There are many tools available in market which could be used for processing of big data. But the main focus on this chapter is on Apache Hadoop which could be regarded as an open source software based framework which could be efficiently deployed for processing, storing, analyzing, and to produce meaningful insights from large sets of data. It is always said that if exponential increase of data is processing challenge then Hadoop could be considered as one of the effective solution for processing, managing, analyzing, and storing this big data. Hadoop versions and components are also illustrated in the later section of the paper. This chapter majorly focuses on the technique, methodology, components, and methodologies adopted by Apache Hadoop software framework for big data processing.

scholarly journals Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Chen
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Integration ◽  
Manufacturing System ◽  
Fog Computing ◽  
Relevant Literature ◽  
Fast Response ◽  
Computing Methods ◽  
Computer Algorithms ◽  
Huge Impact

Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.

Emerging Trends and Techniques in Cloud-Based Data Analytics

2021 ◽  
pp. 37-47
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Lavanya S. ◽  
Sowmya H. D. ◽  
Divya K.
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Huge Data ◽  
Emerging Trends ◽  
Media Applications

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

A Review of Cyber-Physical System Research Relevant to the Emerging IT Trends: Industry 4.0, IoT, Big Data, and Cloud Computing

2017 ◽  
Vol 02 (03) ◽  
pp. 1750011 ◽  
Author(s):  
Jin Ho Kim
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Research Trends ◽  
Cyber Physical System ◽  
System Research ◽  
Research Topics ◽  
Physical Systems ◽  
Emerging Trends

Recently, there has been an explosive growth in the development and implementation of various Cyber-Physical Systems (CPS). Accordingly, CPS-related research and advancements in CPS technologies have increasingly been part of the emerging trends in IT areas such as Internet of Things (IoT), Big Data, cloud computing, and Industry 4.0. However, there are only a few research efforts that identify the comprehensive CPS research trends relevant to the emerging IT trends. Therefore, the aim of this paper is to explore what CPS research topics are related to the emerging IT trends and to investigate how industries have implemented CPS technologies.

Emerging trends, issues and challenges in Internet of Things, Big Data and cloud computing

2018 ◽  
Vol 87 ◽  
pp. 416-419 ◽  
Author(s):  
Anna Kobusińska ◽  
Carson Leung ◽  
Ching-Hsien Hsu ◽  
Raghavendra S. ◽  
Victor Chang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Emerging Trends

Fog Computing Architecture for Scalable Processing of Geospatial Big Data

2020 ◽  
Vol 11 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Rakesh K. Lenka ◽  
Harishchandra Dubey ◽  
Kunal Mankodiya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Case Studies ◽  
Information Management ◽  
Fog Computing ◽  
Geospatial Data ◽  
Raspberry Pi ◽  
Ganga Basin ◽  
Tourism Information

Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management of geospatial big data employing fog computing. The authors developed a prototype of GeoFog architecture using Intel Edison and Raspberry Pi devices. The authors implemented some of the open source compression methods for reducing the data transmission overload in the cloud. Proposed architecture performs data compression and overlay analysis of data. The authors further discussed the improvement in scalability and time analysis using proposed GeoFog architecture and Geospark tool. Discussed results show the merit of fog computing that holds an enormous promise for enhanced analysis of geospatial big data in river Ganga basin and tourism information management scenario.

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