scholarly journals BIG DATA IN CLOUD COMPUTING: A LITERATURE REVIEW

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Neelay Jagani ◽  
Parthil Jagani ◽  
Suril Shah
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Supply Chain ◽  
Literature Review ◽  
Data Storage ◽  
Supply Chain Planning ◽  
Finance Management ◽  
Process Trends

—Big Data and Cloud Computing are two of the most important technologies of the day. Since data is being generated exponentially every day, Big Data has gained a lot of significance in any technology. The daily explosion of data means that it’s better to have big data included in the applications. Whereas cloud computing is allowing users to use platforms according to their time, convenience and affordability. It is providing users ability to collaborate and work efficiently more than ever. Combining these two technologies can give a hands down advantage to the users in terms of knowledge and efficiency. Big Data, when used in cloud computing has applications in different fields such as Finance, Management, supply chain, planning, data storage, warehouses and many more. In this paper, we have discussed Big Data implementation and application in Cloud Computing. 4 V’s in big data can be applied in Cloud computing to get better performance, higher input details, better insights, reliable and secure platforms at comparatively lower costs. Different analytics, technology involved in coupling of big data with cloud computing, the challenges involved in this process, trends applications of the domain and security factors involved are discussed in this paper

scholarly journals Supply Chain 4.0 challenges

2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Felipe de Campos Martins ◽  
Alexandre Tadeu Simon ◽  
Renan Stenico de Campos
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Supply Chain ◽  
Internet Of Things ◽  
Literature Review ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Legal Challenges ◽  
New Industry

Abstract: The Supply Chain has undergone major transformations due to the need to implement new Industry 4.0 technologies, such as Internet of Things, Big Data, Cyber-Physical Systems and Cloud Computing. Thanks to these technologies, as well as to their subsystems and components, full integration of the supply chain is becoming possible. However, it is observed that the real impacts of Industry 4.0 technologies, rather positive or negative, are not yet totally clear and identified. This paper aims to identify and present an analysis of the challenges and obstacles that Industry 4.0 technologies may cause in the Supply Chain. For this, the most relevant papers on the topic were selected and analyzed through a systematic literature review. Twenty challenges grouped into four macrogroups were identified: (1) technical challenges, (2) financial, environmental and legal challenges, (3) technological challenges, and (4) sociocultural challenges. It should be noted that these challenges require greater attention and more in-depth studies on the part of the academy to support industry in order to mitigate them and thus allow better use of the available technological resources and optimize the performance of Supply Chain operations.

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.

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.

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.

A Study on Recent Trends in Cloud-Based Data Processing for IoT Era

2021 ◽  
pp. 2531-2557
Author(s):  
John Shiny J. ◽  
Karthikeyan P.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Storage ◽  
Mobile Phones ◽  
Advance Research ◽  
Recent Trends ◽  
Machine To Machine Communication ◽  
Machine Communication ◽  
Big Data Storage

Cloud computing has become one of the most important technologies in our day-to-day lives. The computing resources are delivered to the customers based on subscription basis via internet. Big data storage and processing are main application of cloud. Furthermore, the development of internet of things provides the platform for interconnecting devices over internet. This includes everything from mobile phones, washing machines, lamps, headphones, wearable devices, and everything else we never think of. This enables machine-to-machine communication, also applies to the components of the machine. The main objective of this chapter is to give an overview of cloud computing, big data, and internet of things and the advance research topics.

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

scholarly journals An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 151465-151473
Author(s):  
Han Yu ◽  
Xiuqing Lu ◽  
Zhenkuan Pan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Public Auditing ◽  
Big Data Storage
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