SLA-Based Resource Scheduling for Big Data Analytics as a Service in Cloud Computing Environments

2015 ◽  
Author(s):  
Yali Zhao ◽  
Rodrigo N. Calheiros ◽  
Graeme Gange ◽  
Kotagiri Ramamohanarao ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Resource Scheduling ◽  
Computing Environments

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 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.

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