A Theoretical Review of Cloud Computing Services in Big Data Analytics

2021 ◽  
Author(s):  
Yuen Neng Chak ◽  
Muhammad Ehsan Rana
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computing Services ◽  
Cloud Computing Services

Big Data and Cloud Computing

2018 ◽  
pp. 1-28 ◽  
Author(s):  
Marcus Tanque ◽  
Harry J Foxwell
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Chain Management ◽  
Computing Services ◽  
Cloud Computing Services

Big data and cloud computing are transforming information technology. These comparable technologies are the result of dramatic developments in computational power, virtualization, network bandwidth, availability, storage capability, and cyber-physical systems. The crossroads of these two areas, involves the use of cloud computing services and infrastructure, to support large-scale data analytics research, providing relevant solutions or future possibilities for supply chain management. This chapter broadens the current posture of cloud computing and big data, as associate with the supply chain solutions. This chapter focuses on areas of significant technology and scientific advancements, which are likely to enhance supply chain systems. This evaluation emphasizes the security challenges and mega-trends affecting cloud computing and big data analytics pertaining to supply chain management.

scholarly journals A Review Paper On Big Data Analytics in Cloud

2021 ◽  
pp. 477-483
Author(s):  
Shaheen Mohsin Ansari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Review Paper ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Choice Data ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Data As A Service ◽  
Cloud Technologies

The amount of data produced in the enterprise is increasing. Any industry will have to cope with exploding data volumes in the future, which will accelerate exponential data growth. It is critical to use a cost-effective, flexible approach for storing and analyzing this data. As a service to big data, the cloud will offer storage, platform, and software capabilities. Big data and cloud technologies are combining to make big data analytics in the cloud a viable choice. Data Analytics as a Service is another name for Cloud for Big Data Analytics. In this review paper we will get to know how big data analytics used cloud computing services for better performance or experience with their benefits, challenges and so on.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

Application of Big Data Analytics via Cloud Computing

2016 ◽  
Author(s):  
Yunus Yetis ◽  
Ruthvik Goud Sara ◽  
Berat A. Erol ◽  
Halid Kaplan ◽  
Abdurrahman Akuzum ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals IoT Based Smart Garage Vehicle Washing Water Nestor

2020 ◽  
Vol 8 (5) ◽  
pp. 3521-3525
Keyword(s):  
Water Pollution ◽  
Cloud Computing ◽  
Big Data ◽  
Smart Cities ◽  
Human Life ◽  
Research Paper ◽  
Proper Treatment ◽  
Computing Services ◽  
Cloud Computing Services ◽  
The Government

Water is critical part of the human life. In most of the developing nation, water pollution is one of the bigger mess. These issues can be handled strictly by the Government organization, by implementing tougher action rules to the industries, were the water are released without any proper treatment. Where each industries (or) smart cities, should take up self-initiative responsibility for proper treatment of the polluted out flow water. In our research paper, we are not focusing on the wider area of the water pollution; our focus is limited within the smart cities vehicle washing garages. In very smart cities, were a regular multiple vehicles washing is done in the garage, our research paper will focus on the out flow of the populated water from these vehicle washing garages. Our design and implantation process is simpler and straightforward approach. Were we will monitor of the water quality; and how much level of the water is populated, and it requires at what level of the treatment. These process can be easily automated using the multiple IOT (internet of things) based sensors, the data can be streamed into the Big Data lake (or) it can be directly pushed into the cloud computing services for generating the real time graphs and analyses report instantly. These data collected in the Big Data lake (or) cloud computing services, can be used for detail analyses for research purpose. We will incorporate the block chain concept to keep track of the smart garage location address and the detail information of the number of garage in the smart cities details in the form of the blocks.

scholarly journals The Smart Triad : Big Data Analytics , Cloud Computing and Internet of Things to shape the Smart Home, Smart City, Smart Business & Smart Country

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3594-3600 ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Smart Home ◽  
Service Providers ◽  
Irrigation System ◽  
Electronic Devices ◽  
Big Data Analytics ◽  
User Privacy

Big data analytics, cloud computing & internet of things are a smart triad which have started shaping our future towards smart home, city, business, country. Internet of things is a convergence of intelligent networks, electronic devices, and cloud computing. The source of big data at different connected electronic devices is stored on cloud server for analytics. Cloud provides the readymade infrastructure, remote processing power to consumers of internet of things. Cloud computing also gives device manufacturers and service providers access to ―advanced analytics and monitoring‖, ―communication between services and devices‖, ―user privacy and security‖. This paper, presents an overview of internet of things, role of cloud computing & big data analytics towards IoT. In this paper IoT enabled automatic irrigation system is proposed that saves data over ―ThingSpeak‖ database an IoT analytics platform through ESP8266 wifi module. This paper also summarizes the application areas and discusses the challenges of IoT.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

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