Private cloud computing system based on dynamic service adaptable to large-scale data processing

Popularity for the term Cloud-Computing has been increasing in recent years. In addition to the SQL technique, Map-Reduce, a programming model that realizes implementing large-scale data processing, has been a hot topic that is widely discussed through many studies. Many real-world tasks such as data processing for search engines can be parallel-implemented through a simple interface with two functions called Map and Reduce. We focus on comparing the performance of the Hadoop implementation of Map-Reduce with SQL Server through simulations. Hadoop can complete the same query faster than SQL Server. On the other hand, some concerned factors are also tested to see whether they would affect the performance for Hadoop or not. In fact more machines included for data processing can make Hadoop achieve a better performance, especially for a large-scale data set.

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A cloud-computing platform for developing and evaluating vocal biomarkers based on home audio recordings: Resources for large-scale data processing and analysis

The Journal of the Acoustical Society of America ◽

10.1121/1.5147769 ◽

2020 ◽

Vol 148 (4) ◽

pp. 2791-2791

Author(s):

Gordon Ramsay

Keyword(s):

Cloud Computing ◽

Data Processing ◽

Large Scale ◽

Computing Platform ◽

Large Scale Data ◽

Audio Recordings ◽

Cloud Computing Platform ◽

Large Scale Data Processing ◽

Scale Data

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Large-Scale Data Processing Using MapReduce in Cloud Computing Environment

International Journal on Web Service Computing ◽

10.5121/ijwsc.2012.3401 ◽

2012 ◽

Vol 3 (4) ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Samira Daneshyar

Keyword(s):

Cloud Computing ◽

Data Processing ◽

Large Scale ◽

Computing Environment ◽

Cloud Computing Environment ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Scale Data

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Research on large scale data processing technology based on cloud computing

Proceedings of the 4th International Conference on Information Technology and Management Innovation ◽

10.2991/icitmi-15.2015.120 ◽

2015 ◽

Author(s):

Qiuhua Wen

Keyword(s):

Cloud Computing ◽

Data Processing ◽

Large Scale ◽

Processing Technology ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Scale Data

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Teaching large scale data processing

Proceedings of the 1st ACM Summit on Computing Education in China on First ACM Summit on Computing Education in China - SCE '08 ◽

10.1145/1517632.1517635 ◽

2008 ◽

Author(s):

Kang Chen ◽

Yubing Yin ◽

Weimin Zheng

Keyword(s):

Data Processing ◽

Large Scale ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Scale Data

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Advanced monitoring techniques for a large‐scale data‐processing network

Campus-Wide Information Systems ◽

10.1108/10650740810921448 ◽

2008 ◽

Vol 25 (5) ◽

pp. 287-300 ◽

Cited By ~ 1

Author(s):

B. Martin ◽

A. Al‐Shabibi ◽

S.M. Batraneanu ◽

Ciobotaru ◽

G.L. Darlea ◽

...

Keyword(s):

Data Processing ◽

Large Scale ◽

Monitoring Techniques ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Processing Network ◽

Scale Data

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Large scale data processing in real world: From analytics to predictions

2014 14th International Conference on Advances in ICT for Emerging Regions (ICTer) ◽

10.1109/icter.2014.7083870 ◽

2014 ◽

Author(s):

Srinath Perera

Keyword(s):

Data Processing ◽

Real World ◽

Large Scale ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Scale Data

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BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2012.236 ◽

2014 ◽

Vol 26 (6) ◽

pp. 1316-1331 ◽

Cited By ~ 6

Author(s):

Gang Chen ◽

Tianlei Hu ◽

Dawei Jiang ◽

Peng Lu ◽

Kian-Lee Tan ◽

...

Keyword(s):

Data Processing ◽

Large Scale ◽

Peer To Peer ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Processing Platform ◽

Scale Data

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Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.31.18233 ◽

2018 ◽

Vol 7 (2.31) ◽

pp. 240

Author(s):

S Sujeetha ◽

Veneesa Ja ◽

K Vinitha ◽

R Suvedha

Keyword(s):

Control System ◽

Data Processing ◽

Real Time ◽

Large Scale ◽

Diffusion Control ◽

Qr Code ◽

Healthcare Applications ◽

Large Scale Data ◽

Large Scale Data Processing ◽

Scale Data

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access. The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.

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