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.

scholarly journals INTELLIGENT DEVELOPMENT OF BIG DATA ANALYTICS FOR MANUFACTURING INDUSTRY IN CLOUD COMPUTING

2019 ◽  
Vol 01 (01) ◽  
pp. 13-22 ◽  
Author(s):  
Abul Bashar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Smart Home ◽  
Industrial Development ◽  
Manufacturing Industry ◽  
Big Data Analytics ◽  
City Development ◽  
Real Time Monitoring

Big-data analytics being a useful technique in the analyzing the deeper values hidden inside a huge set of data flow that are generated in our day today lives, has almost become more prominent in variety of applications such as industrial development, smart home to smart city development and security management etc., despite its high potentials the challenges incurred makes it insufficient with certain applications that include a real time monitoring, so the paper proposes the real time monitoring of the developing manufacturing industry by proffering the intelligent big data analytics and cloud computing to present with the maximum possible insights to improvise the process of the manufacturing , by retaining the product consistency, optimal throughput and increasing the productivity.

scholarly journals Wissens- und lernorientierte Instandhaltung unter dem Aspekt zunehmender Digitalisierung

2020 ◽  
Author(s):  
Cornelia Huber ◽  
Theresa Passath ◽  
Hubert Biedermann
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Smart Factory

Zusammenfassung Die Digitalisierung bietet Industrieunternehmen eine Vielzahl von Möglichkeiten und Lösungen (z. B. Big Data Analytics, Cloud-Computing, Internet of Things), die mit zunehmenden Anforderungen an das Wissensmanagement verbunden sind. Ein angepasstes Assetmanagement zur Sicherstellung von Markterfolgsfaktoren sind für die Smart Factory unerlässlich. Ein ganzheitliches wertschöpfungsorientiertes Managementkonzept bietet die Lean Smart Maintenance Philosophie, das den geänderten Umfeldbedingungen Rechnung tragend, eine dynamisch anpassbare Instandhaltungsstrategie ermöglicht. Insbesondere die Ressource Wissen gewinnt in Anbetracht einer ständig steigenden Anlagenkomplexität in Form der wissen- und lernorientierten Instandhaltung an Bedeutung, damit zukünftig der Einsatz neuer Technologien nachhaltig wirtschaftlich ist. In diesem Zusammenhang wird nachstehend eine praxisnahe Vorgehensmethodik präsentiert, die die Externalisierung impliziten Wissens durch die Kombination der Wissensbausteine nach Probst mit dem DMAIC-Zyklus unterstützt. Die Digitalisierung induziert geänderte Anforderungen an die Mitarbeiter und Mitarbeiterinnen; geeignete Qualifizierungsmaßnahmen sollten für die bestmögliche Weiterentwicklung der Humanressourcen frühzeitig erkannt und implementiert werden.

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.

Big data analytics for 6G enabled massive Internet of Things

2021 ◽  
pp. 1-1
Author(s):  
Zhihan Lv ◽  
Ranran Lou ◽  
Jinhua Li ◽  
Amit Kumar Singh ◽  
Houbing Song
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics
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