scholarly journals Study on Development Bottleneck and Service Strategy of China E-government Cloud Platform in the Big Data Era

2019 ◽  
Vol 490 ◽  
pp. 062068
Author(s):  
Hao Cheng ◽  
Bing Zhao ◽  
Xuran Liu
Keyword(s):  
Big Data ◽  
Cloud Platform ◽  
Service Strategy

Low-cost IoT, Big Data, and Cloud Platform for Developing Countries

2017 ◽  
pp. 285-299 ◽  
Author(s):  
Corentin Dupont ◽  
Tomas Bures ◽  
Mehdi Sheikhalishahi ◽  
Congduc Pham ◽  
Abdur Rahim
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Low Cost ◽  
Cloud Platform

scholarly journals Wrinkle-Enabled Highly Stretchable Strain Sensors for Wide-Range Health Monitoring with a Big Data Cloud Platform

2020 ◽  
Vol 12 (38) ◽  
pp. 43009-43017 ◽  
Author(s):  
Jian Huang ◽  
Jian Zhou ◽  
Yangmei Luo ◽  
Gan Yan ◽  
Yi Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Health Monitoring ◽  
Strain Sensors ◽  
Cloud Platform ◽  
Wide Range ◽  
Highly Stretchable

scholarly journals Big Data Management for Cloud-Enabled Geological Information Services

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yueqin Zhu ◽  
Yongjie Tan ◽  
Xiong Luo ◽  
Zhijie He
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Information Services ◽  
Cloud Platform ◽  
Application Development ◽  
Geological Information ◽  
Technology Applications ◽  
Massive Scale ◽  
Application Requirements

Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

scholarly journals A Critique on Heart Diseases Predictive Analytics using Big Data Algorithms

2019 ◽  
Vol 8 (9S2) ◽  
pp. 794-798
Keyword(s):  
Big Data ◽  
Cardiac Disease ◽  
Heart Attack ◽  
Predictive Analytics ◽  
Heart Diseases ◽  
Machine Learning Algorithms ◽  
Risk Level ◽  
Cloud Platform ◽  
Unstructured Information ◽  
Present World

A large volume of both structured and unstructured information is managed by the emerging technology big data. This information is complicated to practice using set records and software techniques. An elite solution is brought in all technologies by using them competently. To improve the prediction of heart diseases earlier and bring more intellectual decisions the big data is potential in healthcare organization. In the present world condition the doctors and experts available are very intricate to forecast the heart diseases. The heart attack has become a remarkable cause of the endless demise worldwide. Heart attack is essential to predict it at an earlier stage to standby the existence of individuals and it is the main source of demise. The primary purpose is to predict the risk level of a person using Big Data algorithms for the cardiac disease. Big Data is primarily designed to provide a national scheme for physicians and patients to login and view Cloud information. Hadoop Map Reduce programming is used to maintain the hospital details. The machine learning algorithms is used to view the precise condition of the patient in its graphical demonstration. Using cloud platform for accessing globally exploitation any browsers in any a part of the globe this application are often enforced.

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