scholarly journals PERANCANGAN APLIKASI KOMPRESI FILE TEKS DENGAN MENERAPKAN ALGORTIMA FIXED LENGTH BINARY ENCODING (FLBE)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Rizka Dwi Pratiwi ◽  
Surya Darma Nasution ◽  
Fadlina Fadlina
Keyword(s):  
Data Compression ◽  
Data Storage ◽  
Compression Ratio ◽  
Data Exchange ◽  
Data Transfer ◽  
Large Data ◽  
Fixed Length ◽  
Binary Encoding ◽  
Compression Data ◽  
Exchange Transactions

The higher activity of data exchange transactions both online and offline raises concerns for some parties, large data sizes result in a waste of memory and slow data transfer and delivery processes. For this reason, a technique is needed to change the size of the data to be smaller. This technique is called compression or better known as data compression. Data compression is a process of converting a set of data into a form of code to save data storage requirements. Fixed Length Binary Encoding Algorithm (FLBE) uses a lossless technique that does not eliminate information at all, only representing some of the same information. The results obtained from the application of Fixed Length Binary Encoding algorithm in the process of compression and decompression include compression capacity, compression ratio and compression and decompression time. In accordance with the results of the experiments carried out, it can be seen that the data originally having a larger size can be compressed well implemented in text files.

scholarly journals GIF IMAGE HARDWARE COMPRESSORS

2021 ◽  
Author(s):  
Ivan Mozghovyi ◽  
Anatoliy Sergiyenko ◽  
Roman Yershov
Keyword(s):  
Data Compression ◽  
Data Storage ◽  
High Speed ◽  
Data Transfer ◽  
Future Research ◽  
Volume Data ◽  
High Speed Data ◽  
Lzw Algorithm ◽  
And Storage ◽  
Speed Data Transmission

Increasing requirements for data transfer and storage is one of the crucial questions now. There are several ways of high-speed data transmission, but they meet limited requirements applied to their narrowly focused specific target. The data compression approach gives the solution to the problems of high-speed transfer and low-volume data storage. This paper is devoted to the compression of GIF images, using a modified LZW algorithm with a tree-based dictionary. It has led to a decrease in lookup time and an increase in the speed of data compression, and in turn, allows developing the method of constructing a hardware compression accelerator during the future research.

scholarly journals IMPROVING THE DATA TRANSMISSION SPEED IN CLOUD MIGRATION BY USING MAPREDUCE FOR BIGDATA

2020 ◽  
Vol 4 (5) ◽  
pp. 73-75
Author(s):  
Naresh P ◽  
Rajyalakshmi P ◽  
Krishna Vempati ◽  
Saidulu D
Keyword(s):  
Data Storage ◽  
Data Transmission ◽  
Data Exchange ◽  
Data Transfer ◽  
Data Migration ◽  
Migration Process ◽  
Huge Amount ◽  
Cloud Migration ◽  
Distributed Cloud ◽  
Transmission Speed

Cloud acts as a data storage and also used for data transfer from one cloud to other. Here data exchange takes place among cloud centers of organizations. At each cloud center huge amount of data was stored, which interns hard to store and retrieve information from it. While migrating the data there are some issues like low data transfer rate, end to end latency issues and data storage issues will occur. As data was distributed among so many cloud centers from single source, will reduces the speed of migration. In distributed cloud computing it is very difficult to transfer the data fast and securely. This paper explores MapReduce within the distributed cloud architecture where MapReduce assists at each cloud. It strengthens the data migration process with the help of HDFS. Compared to existing cloud migration approach the proposed approach gives accurate results interns of speed, time and efficiency.

scholarly journals The Modern Research Data Portal: A design pattern for networked, data-intensive science

2017 ◽  
Author(s):  
Kyle Chard ◽  
Eli Dart ◽  
Ian Foster ◽  
David Shifflett ◽  
Steven Tuecke ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Storage ◽  
Design Pattern ◽  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Large Data ◽  
Research Data ◽  
Control Logic ◽  
Data Portal

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

Nth Order Binary Encoding with Split-Protocol

2018 ◽  
Vol 5 (2) ◽  
pp. 95-118 ◽  
Author(s):  
Bharat S Rawal ◽  
Songjie Liang ◽  
Shiva Gautam ◽  
Harsha Kumara Kalutarage ◽  
P Vijayakumar
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Data Storage ◽  
Network Traffic ◽  
Transmission Rate ◽  
Security And Privacy ◽  
Unique Combination ◽  
Privacy And Security ◽  
Binary Encoding ◽  
Data Explosion

To cope up with the Big Data explosion, the Nth Order Binary Encoding (NOBE) algorithm with the Split-protocol has been proposed. In the earlier papers, the application Split-protocol for security, reliability, availability, HPC have been demonstrated and implemented encoding. This technology will significantly reduce the network traffic, improve the transmission rate and augment the capacity for data storage. In addition to data compression, improving the privacy and security is an inherent benefit of the proposed method. It is possible to encode the data recursively up to N times and use a unique combination of NOBE's parameters to generate encryption keys for additional security and privacy for data on the flight or at a station. This paper describes the design and a preliminary demonstration of (NOBE) algorithm, serving as a foundation for application implementers. It also reports the outcomes of computable studies concerning the performance of the underlying implementation.

scholarly journals The Modern Research Data Portal: A design pattern for networked, data-intensive science

2017 ◽  
Author(s):  
Kyle Chard ◽  
Eli Dart ◽  
Ian Foster ◽  
David Shifflett ◽  
Steven Tuecke ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Storage ◽  
Design Pattern ◽  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Large Data ◽  
Research Data ◽  
Control Logic ◽  
Data Portal

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

scholarly journals Big Data Compression Technology Based on Internet of Vehicles

2019 ◽  
Vol 15 (01) ◽  
pp. 85
Author(s):  
Guohua Xiong
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Compression Ratio ◽  
High Efficiency ◽  
Large Data ◽  
Internet Of Vehicles ◽  
Fixed Threshold ◽  
Data Volume ◽  
Networking Technology ◽  
Vehicle Networking

To ensure the high efficiency of the development of car networking technology, large data compression technology based on car networking was studied. First, RFID technology and vehicle networking, big data technology in vehicle networking, RFID path data compression technology in the Internet of vehicles were introduced. Then, RFID path data compression verification experiments were performed. The results showed that when the data volume was relatively small, there was no obvious change in the compression ratio under the fixed threshold and the threshold change. However, when the amount of data gradually increased, the compression ratio under the condition of changing the threshold was slightly higher than the fixed threshold. Therefore, RFID path big data processing is feasible, and compression technology is efficient.

scholarly journals The Modern Research Data Portal: A design pattern for networked, data-intensive science

2017 ◽  
Author(s):  
Kyle Chard ◽  
Eli Dart ◽  
Ian Foster ◽  
David Shifflett ◽  
Steven Tuecke ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Storage ◽  
Design Pattern ◽  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Large Data ◽  
Research Data ◽  
Control Logic ◽  
Data Portal

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

scholarly journals ANALISIS PERBANDINGAN KINERJA ALGORITMA FIXED LENGTH BINARY ENCODING DAN ALGORITMA ELIAS GAMMA CODE DALAM KOMPRESI FILE TEKS

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dian Pratiwi ◽  
Taronisokhi Zebua
Keyword(s):  
Compression Ratio ◽  
Performance Comparison ◽  
Test Results ◽  
Average Value ◽  
Time Compression ◽  
Fixed Length ◽  
Binary Encoding ◽  
Code Algorithm ◽  
Different Characteristics ◽  
Better Than

Now days, there are many algorithms developed for data compression, but there is no one that is good for compressing various types of files because of different characteristics or file structures. This research explained the result of two compression algorithms in order to know the performance comparison between the Fixed Length Binary Encoding (FLBE) algorithm and the elias gamma code algorithm in compressing text files, especially in format rtf. The parameters being compared are the ratio of compression, compression ratio, redundancy and time. Based on the test results show that the fixed length binary encoding algorithm is better than the elias gamma code algorithm where the average ratio of compression results of fixed length binary encoding algorithm is 1.66 bits while the elias gamma code is 1.62 bits. The average compression ratio of fixed length binary encoding algorithm is 60.9% while Elias Gamma Code is 62.20%. The average value of the redundancy of the fixed length binary encoding algorithm is 39.1% while the gamma code elias is 37.79%. The average time compression value of the fixed length binary encoding algorithm is 16 ms while the elias gamma code is 21 ms.Keywords: comparison, compression, FLBE algorithm, Elias Gamma Code Algorithm, text, rtf

scholarly journals PENGEMBANGAN DAN ANALISIS KOMBINASI RUN LENGTH ENCODING DAN RELATIVE ENCODING UNTUK KOMPRESI CITRA

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Yosia Adi Jaya ◽  
Lukas Chrisantyo ◽  
Willy Sudiarto Raharjo
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Fast Algorithm ◽  
Data Transfer ◽  
Lossless Compression ◽  
Compression Algorithm ◽  
Storage Space ◽  
Run Length ◽  
Relative Value ◽  
Image Test

Data Compression can save some storage space and accelerate data transfer. Among many compression algorithm, Run Length Encoding (RLE) is a simple and fast algorithm. RLE can be used to compress many types of data. However, RLE is not very effective for image lossless compression because there are many little differences between neighboring pixels. This research proposes a new lossless compression algorithm called YRL that improve RLE using the idea of Relative Encoding. YRL can treat the value of neighboring pixels as the same value by saving those little differences / relative value separately. The test done by using various standard image test shows that YRL have an average compression ratio of 75.805% for 24-bit bitmap and 82.237% for 8-bit bitmap while RLE have an average compression ratio of 100.847% for 24-bit bitmap and 97.713% for 8-bit bitmap.

scholarly journals The Modern Research Data Portal: a design pattern for networked, data-intensive science

2018 ◽  
Vol 4 ◽  
pp. e144 ◽  
Author(s):  
Kyle Chard ◽  
Eli Dart ◽  
Ian Foster ◽  
David Shifflett ◽  
Steven Tuecke ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Storage ◽  
Design Pattern ◽  
High Speed ◽  
High Performance ◽  
Data Transfer ◽  
Large Data ◽  
Research Data ◽  
Control Logic ◽  
Data Portal

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

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