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.

scholarly journals Analyzation and Comparison of Cloud Computing and Data Mining Techniques: Big Data and Impact of Blockchain

2021 ◽  
Vol 9 (11) ◽  
pp. 712-721
Author(s):  
Adarsh Bhandari
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Scale Up ◽  
Big Data Analytics ◽  
Multiple Sources ◽  
Huge Data ◽  
Amazon Web Services ◽  
Tools And Techniques ◽  
The Impact

Abstract: With the rapid escalation of data driven solutions, companies are integrating huge data from multiple sources in order to gain fruitful results. To handle this tremendous volume of data we need cloud based architecture to store and manage this data. Cloud computing has emerged as a significant infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and scale up the products. Even today heavy applications are deployed on cloud and managed specially at AWS eliminating the need for error prone manual operations. This paper demonstrates about certain cloud computing tools and techniques present to handle big data and processes involved while extracting this data till model deployment and also distinction among their usage. It will also demonstrate, how big data analytics and cloud computing will change methods that will later drive the industry. Additionally, a study is presented later in the paper about management of blockchain generated big data on cloud and making analytical decision. Furthermore, the impact of blockchain in cloud computing and big data analytics has been employed in this paper. Keywords: Cloud Computing, Big Data, Amazon Web Services (AWS), Google Cloud Platform (GCP), SaaS, PaaS, IaaS.

Big Data Analytics for Multipurpose Social Media Applications

2017 ◽  
pp. 289-306
Author(s):  
Bhavani Thuraisingham ◽  
Mohammad Mehedy Masud ◽  
Pallabi Parveen ◽  
Latifur Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Media Applications

Data Management and Big Data Analytics

2019 ◽  
pp. 1-23
Author(s):  
Vellingiri Jayagopal ◽  
Basser K. K.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Digital Images ◽  
Big Data Analytics ◽  
Database Systems ◽  
The Internet ◽  
Processing Capacity

The internet is creating 2.5 quintillion bytes of data, and according to the statistics, the percentage of data that has been generated from last two years is 90%. This data comes from many industries like climate information, social media sites, digital images and videos, and purchase transactions. This data is big data. Big data is the data that exceeds storage and processing capacity of conventional database systems. Data in today's world (big data) is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc. About 80% of data captured today is unstructured. All this data is also big data.

Data Management and Big Data Analytics

2022 ◽  
pp. 1614-1633
Author(s):  
Vellingiri Jayagopal ◽  
Basser K. K.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Digital Images ◽  
Big Data Analytics ◽  
Database Systems ◽  
The Internet ◽  
Processing Capacity

The internet is creating 2.5 quintillion bytes of data, and according to the statistics, the percentage of data that has been generated from last two years is 90%. This data comes from many industries like climate information, social media sites, digital images and videos, and purchase transactions. This data is big data. Big data is the data that exceeds storage and processing capacity of conventional database systems. Data in today's world (big data) is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc. About 80% of data captured today is unstructured. All this data is also big data.

scholarly journals An Extensive Analysis on Big Data

2021 ◽  
pp. 591-595
Author(s):  
M. Lakshmi Priya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Extensive Analysis ◽  
Huge Data ◽  
Heterogeneous Datasets ◽  
Different Sources ◽  
The Internet Of Things

In recent years, data has rapidly developed because of the growth of the internet, the internet of things, cloud computing, and various technologies. The size of data processed and transmitted over the internet is drastically increasing. Big data refers to a database that handles huge data in real-time yet growing exponentially with time. Big data analytics uses advanced techniques on large heterogeneous datasets that are collected from different sources, and in various sizes. Big data can manage and process the data beyond the ability of a relational database.

scholarly journals Digital security vulnerabilities and threats implications for financial institutions deploying digital technology platforms and application: FMEA and FTOPSIS analysis

2021 ◽  
Vol 7 ◽  
pp. e658
Author(s):  
Abeeku Sam Edu ◽  
Mary Agoyi ◽  
Divine Agozie
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Digital Technology ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Effect Analysis ◽  
Adequate Knowledge ◽  
Business Operations ◽  
Security Audits

Digital disruptions have led to the integration of applications, platforms, and infrastructure. They assist in business operations, promoting open digital collaborations, and perhaps even the integration of the Internet of Things (IoTs), Big Data Analytics, and Cloud Computing to support data sourcing, data analytics, and storage synchronously on a single platform. Notwithstanding the benefits derived from digital technology integration (including IoTs, Big Data Analytics, and Cloud Computing), digital vulnerabilities and threats have become a more significant concern for users. We addressed these challenges from an information systems perspective and have noted that more research is needed identifying potential vulnerabilities and threats affecting the integration of IoTs, BDA and CC for data management. We conducted a step-by-step analysis of the potential vulnerabilities and threats affecting the integration of IoTs, Big Data Analytics, and Cloud Computing for data management. We combined multi-dimensional analysis, Failure Mode Effect Analysis, and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution to evaluate and rank the potential vulnerabilities and threats. We surveyed 234 security experts from the banking industry with adequate knowledge in IoTs, Big Data Analytics, and Cloud Computing. Based on the closeness of the coefficients, we determined that insufficient use of backup electric generators, firewall protection failures, and no information security audits are high-ranking vulnerabilities and threats affecting integration. This study is an extension of discussions on the integration of digital applications and platforms for data management and the pervasive vulnerabilities and threats arising from that. A detailed review and classification of these threats and vulnerabilities are vital for sustaining businesses’ digital integration.

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.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique

scholarly journals EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

2021 ◽  
pp. 074391562199967
Author(s):  
Raffaello Rossi ◽  
Agnes Nairn ◽  
Josh Smith ◽  
Christopher Inskip
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Volume Content ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
The Internet ◽  
Policy Recommendations ◽  
Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

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