DETECTION AND ELIMINATION OF FAKE REVIEWS FROM REAL-TIME DATA USING CLOUD COMPUTING

2017 ◽  
Vol 4 (05) ◽  
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Time Data ◽  
Real Time Data ◽  
Fake Reviews

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

scholarly journals Adoption of Cloud-based E-Health Record through the Technology, Organization and Environment Perspective

2018 ◽  
Vol 7 (4.35) ◽  
pp. 609 ◽  
Author(s):  
Hidayah Sulaiman ◽  
Asma Magaireh ◽  
Rohaini Ramli
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Access ◽  
Healthcare Setting ◽  
Easy Access ◽  
Management Support ◽  
Computing Technology ◽  
Time Data ◽  
Real Time Data ◽  
The Impact

With the ever increasing cost of investing in technological innovations and the amount of patient data to be processed on daily basis, healthcare organizations are in dire need for solutions that could provide easy access and better management of real time data with lower cost.  The emerging trend of organizations optimizing cost in investing less on physical hardware has brought about the use of cloud computing technology in various industries including healthcare.  The use of cloud computing technology has brought better efficiency in providing real time data access, bigger storage capacity and reduction of cost in terms of maintenance. Although numerous benefits have been publicized for organizations to adopt the technology, nevertheless the rate of adoption is still at is infancy. Hence, this study explores factors that may affect the adoption of cloud-based technology particularly within the healthcare context. A quantitative study was conducted through the distribution of survey in Jordanian healthcare facilities. The survey was conducted to gauge the understanding of cloud-based EHR concepts identified through literature and validate the factors that could potentially provide an impact towards the cloud-based EHR adoption. The theoretical underpinnings of Technology-Organization-Environment (TOE) were investigated in studying the impact towards the adoption of cloud-based EHR. Results indicate that Technology-Organization-Environment factors such as privacy, reliability, security, top management support, organizational readiness, competition and regulatory environment are critical factors towards the adoption of cloud technology within a healthcare setting.

scholarly journals Fog Computing Mitigate Limitations of Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 1732-1736
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Public Cloud ◽  
Time Data ◽  
Security Issues ◽  
Computing Model ◽  
Real Time Data ◽  
Real Time Data Processing

In today’s world, cloud computing is the most exciting and advanced technology. It came into existence with lots of advantages, but cloud-only computing has some disadvantages also like latency in real-time data processing, network congestion, less bandwidth utilization, fault tolerance, and security issues in public cloud. To address the issue of real-time data-processing and security in public cloud new computing model are used which is known as Fog Computing. It is nearer to the client or edge so that it can reduce the latency in real time data-processing and security in public cloud using techniques like user profiling and decoying technique. Fog Computing help us to overcome the latency and security issues of cloud computing. It reduces cloud latency in real time data-processing because fog computing model is nearer to the edge devices. It also improves cloud security in the public cloud.

scholarly journals Cloud Computing and Internet of Things in the Evaluation of Ecological Environment Quality in Rural Tourist Areas in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ling Ding ◽  
Yezhang Liang
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Rural Tourism ◽  
Ecological Environment ◽  
Ecological Health ◽  
Time Data ◽  
Environment Quality ◽  
Real Time Data ◽  
Gis Platform

With the increase of tourist environment, the real-time monitoring of ecological environment has become a concern. This study mainly discusses the application of cloud computing and Internet of things in the evaluation of ecological environment quality of rural tourism areas in a smart city. In this study, the real-time monitoring of the atmosphere, water, and meteorological data is collected through the GPRS data transmission module and then sent back to the local server by the GPRS network, and the obtained non-real-time and real-time data are used to establish the ecological monitoring database, the database analysis of its information, and get real-time data, monthly data, and longer cycle data. In the cloud GIS platform, there are multiple subnodes. The split tasks can be processed by each subnode through a map, and the results after processing can be summarized through reduce, which completes the implementation process of the whole idea of map reduce. Monitoring station management is mainly to establish monitoring stations in rural tourism areas and collect first-hand environmental monitoring data by using temperature, humidity, infrared, ultrasonic, and other sensors and cameras. The monitoring objects are the air quality, water quality, meteorology, etc. of the scenic area, mainly showing the location of monitoring stations and the placement of sensors. At the same time, an LED screen is set at the monitoring station to display the air quality data of the scenic spot. The data content is introduced into the DPSIR model, combined with social and economic data; according to the ecological health grading evaluation standard, the evaluation score and health grade are obtained and the ecological health status of rural tourism area is judged and evaluated. When the amount of data is less than 500 MB, there is little difference between the storage speed of the cloud GIS platform and single machine, but with the continuous increase of the amount of data, the storage speed of the cloud GIS platform is significantly higher than that of a single machine. This study is helpful in improving the ecological environment quality of rural tourism areas.

Trends and Technologies in Big Data Processing

2021 ◽  
pp. 42-67
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
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