Evaluation of Data Storage Patterns in Microservices Archicture

2020 ◽  
Author(s):  
Munonye K ◽  
Martinek P
Keyword(s):  
Data Storage ◽  
Evaluation Of Data ◽  
Storage Patterns

Research on Data Storage Structure and Management Model in Hierarchical Storage

2010 ◽  
Vol 121-122 ◽  
pp. 198-203
Author(s):  
Yan Chun ◽  
Sheng Xi Li ◽  
Ya Zhou Li
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Management Model ◽  
Storage Structure ◽  
Mass Data ◽  
Hierarchical Storage ◽  
Evaluation Of Data ◽  
Structure Evaluation

As hierarchical storage is one of the highlights in researching and realizing of mass data storage, this paper mainly describes a study on data storage structure, evaluation of data importance and data management model of hierarchical storage.

scholarly journals Performance Evaluation of Data Compression Systems Applied to Satellite Imagery

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Lilian N. Faria ◽  
Leila M. G. Fonseca ◽  
Max H. M. Costa
Keyword(s):  
Performance Evaluation ◽  
Image Quality ◽  
Data Storage ◽  
Compression Ratio ◽  
Rate Distortion ◽  
Quality Analysis ◽  
Sensing Applications ◽  
Quantitative Performance ◽  
Jpeg Xr ◽  
Evaluation Of Data

Onboard image compression systems reduce the data storage and downlink bandwidth requirements in space missions. This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. Prediction-based compression systems, such as DPCM and JPEG-LS, and transform-based compression systems, such as CCSDS-IDC and JPEG-XR, were tested over twenty multispectral (5-band) images from CCD optical sensor of the CBERS-2B satellite. Performance evaluation of these algorithms was conducted using both quantitative rate-distortion measurements and subjective image quality analysis. The PSNR, MSSIM, and compression ratio results plotted in charts and the SSIM maps are used for comparison of quantitative performance. Broadly speaking, the lossless JPEG-LS outperforms other lossless compression schemes, and, for lossy compression, JPEG-XR can provide lower bit rate and better tradeoff between compression ratio and image quality.

scholarly journals Performance Evaluation of Data Intensive Computing In the Cloud

2014 ◽  
Vol 4 (2) ◽  
pp. 34-47
Author(s):  
Sanjay P. Ahuja ◽  
Bhagavathi Kaza
Keyword(s):  
Data Storage ◽  
Performance Effect ◽  
Data Intensive ◽  
Amazon Ec2 ◽  
Active Research ◽  
Increasing Demand ◽  
Primary Focus ◽  
Data Layers ◽  
Evaluation Of Data ◽  
Data Intensive Applications

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

Evaluation of data storage layer thickness best fitted for digital data read-out procedure from hard x-ray optical memory

2009 ◽  
Author(s):  
Hakob P. Bezirganyan ◽  
Siranush E. Bezirganyan ◽  
Petros H. Bezirganyan, Jr. ◽  
Hayk H. Bezirganyan, Jr.
Keyword(s):  
Layer Thickness ◽  
Data Storage ◽  
Optical Memory ◽  
Digital Data ◽  
X Ray ◽  
Evaluation Of Data

scholarly journals Performance Evaluation of Data Intensive Computing In the Cloud

2015 ◽  
pp. 1901-1914 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Bhagavathi Kaza
Keyword(s):  
Data Storage ◽  
Performance Effect ◽  
Data Intensive ◽  
Amazon Ec2 ◽  
Active Research ◽  
Increasing Demand ◽  
Primary Focus ◽  
Data Layers ◽  
Evaluation Of Data ◽  
Data Intensive Applications

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

scholarly journals Pelatihan Pemanfaatan Teknologi Google Drive dan Blogs bagi Pengawas Sekolah Dasar

2021 ◽  
Vol 5 (2) ◽  
pp. 330-334
Author(s):  
Erpidawati ◽  
Novelti
Keyword(s):  
Developing Countries ◽  
Data Management ◽  
Community Service ◽  
Data Storage ◽  
Research Results ◽  
New Knowledge ◽  
The World ◽  
Google Drive ◽  
Storage Patterns

In the world of education, we must quickly obtain information and be fast in sending the requested information. Indonesia is one of the developing countries that is trying to take advantage of this digitization system. With the digitization system, it is hoped that it will make it easier to send and convey information to relevant agencies in need. This activity is carried out with the aim of providing training and technical implementation so that school supervisors can take advantage of supporting media and provide useful new knowledge. The method used in this community service is the seminar method. The results obtained after this activity were that the participants were able to apply google drive and create blogs independently, in turn, and under supervision. The conclusion of the research results is that school supervisors get new knowledge, are able to operate and create google drives and blogs, understand that adding data storage patterns and data management can be done easily and efficiently.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

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