A Lossless Data Compression Algorithm for Real-time Database

2006 ◽  
Author(s):  
Wenjun Huang ◽  
Weimin Wang ◽  
Hui Xu
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Compression Algorithm ◽  
Lossless Data Compression ◽  
Real Time Database

Study on Data Compression Algorithm Based on Semantic Analysis

2013 ◽  
Vol 842 ◽  
pp. 712-716
Author(s):  
Qi Hong ◽  
Xiao Lei Lu
Keyword(s):  
Data Compression ◽  
Semantic Analysis ◽  
Traditional Approach ◽  
Basic Element ◽  
Compression Algorithm ◽  
Huffman Coding ◽  
Text Compression ◽  
C Language ◽  
Lossless Data Compression ◽  
Compression Coding

As a lossless data compression coding, Huffman coding is widely used in text compression. Nevertheless, the traditional approach has some deficiencies. For example, same compression on all characters may overlook the particularity of keywords and special statements as well as the regularity of some statements. In terms of this situation, a new data compression algorithm based on semantic analysis is proposed in this paper. The new kind of method, which takes C language keywords as the basic element, is created for solving the text compression of source files of C language. The results of experiment show that the compression ratio has been improved by 150 percent roughly in this way. This method can be promoted to apply to text compression of the constrained-language.

Design of Real-Time Lossless Compression System Based on DSP and FPGA

2012 ◽  
Vol 433-440 ◽  
pp. 4173-4177
Author(s):  
Jian Hu Zhan ◽  
Wen Yi Liu
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Removal Rate ◽  
Lossless Compression ◽  
Telemetry System ◽  
Compression Device ◽  
Hardware System ◽  
Data Capacity ◽  
Noise Data ◽  
Lossless Data Compression

The application of the lossless data compression technology in the filed of telemetry system is discussed in this paper. Based on the ARC algorithm, a real-time lossless data compression technology is proposed. By combining the TMS320C6416 and XC3S200AN FPGA, this paper designs a real-time lossless data compression device hardware system. 2048 bytes of some telemetry noise data can be compressed in 5.64ms in this system and the compression removal rate reaches 78%. What’s more, the system has solved the problem of data capacity and speed during the process of data compression , which greatly improves the efficiency of data compression.

scholarly journals USE OF NEW EFFICIENT LOSSLESS DATA COMPRESSION METHOD IN TRANSMITTING ENCRYPTED BAPTISTA SYMMETRIC CHAOTIC CRYPTOSYSTEM DATA

2016 ◽  
Vol 78 (6-4) ◽  
Author(s):  
Muhamad Azlan Daud ◽  
Muhammad Rezal Kamel Ariffin ◽  
S. Kularajasingam ◽  
Che Haziqah Che Hussin ◽  
Nurliyana Juhan ◽  
...  
Keyword(s):  
Data Compression ◽  
Data Transmission ◽  
Compression Algorithm ◽  
Huffman Coding ◽  
Compression Method ◽  
Compression Technique ◽  
Chaotic Dynamical System ◽  
Fast Encoding ◽  
Compression Mechanism ◽  
Lossless Data Compression

A new compression algorithm used to ensure a modified Baptista symmetric cryptosystem which is based on a chaotic dynamical system to be applicable is proposed. The Baptista symmetric cryptosystem able to produce various ciphers responding to the same message input. This modified Baptista type cryptosystem suffers from message expansion that goes against the conventional methodology of a symmetric cryptosystem. A new lossless data compression algorithm based on theideas from the Huffman coding for data transmission is proposed.This new compression mechanism does not face the problem of mapping elements from a domain which is much larger than its range.Our new algorithm circumvent this problem via a pre-defined codeword list.  The purposed algorithm has fast encoding and decoding mechanism and proven analytically to be a lossless data compression technique.

Real-time lossless data compression techniques for long-pulse operation

2007 ◽  
Vol 82 (5-14) ◽  
pp. 1301-1307 ◽  
Author(s):  
J. Vega ◽  
M. Ruiz ◽  
E. Sánchez ◽  
A. Pereira ◽  
A. Portas ◽  
...  
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Pulse Operation ◽  
Lossless Data Compression ◽  
Long Pulse

A Hardware Implementation of Real Time Lossless Data Compression and Decompression Circuits

2015 ◽  
Vol 719-720 ◽  
pp. 554-560
Author(s):  
Le Yang ◽  
Zhao Yang Guo ◽  
Shan Shan Yong ◽  
Feng Guo ◽  
Xin An Wang
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Hardware Implementation ◽  
Storage Capacity ◽  
Time Data ◽  
Hardware Resource ◽  
Hardware Description ◽  
Lossless Data Compression ◽  
Timing Simulation ◽  
Lzw Algorithm

This paper presents a hardware implementation of real time data compression and decompression circuits based on the LZW algorithm. LZW is a dictionary based data compression, which has the advantage of fast speed, high compression, and small resource occupation. In compression circuit, the design creatively utilizes two dictionaries alternately to improve efficiency and compressing rate. In decompression circuit, an integrated State machine control module is adopted to save hardware resource. Through hardware description and language programming, the circuits finally reach function simulation and timing simulation. The width of data sample is 12bits, and the dictionary storage capacity is 1K. The simulation results show the compression and decompression circuits have complete function. Compared to software method, hardware implementation can save more storage and compressing time. It has a high practical value in the future.

scholarly journals Hybrid indexes for repetitive datasets

2014 ◽  
Vol 372 (2016) ◽  
pp. 20130137 ◽  
Author(s):  
H. Ferrada ◽  
T. Gagie ◽  
T. Hirvola ◽  
S. J. Puglisi
Keyword(s):  
Dna Sequencing ◽  
Data Compression ◽  
Simple Technique ◽  
Upper Bounds ◽  
Compression Algorithm ◽  
Original Text ◽  
Human Genomes ◽  
Lossless Data Compression

Advances in DNA sequencing mean that databases of thousands of human genomes will soon be commonplace. In this paper, we introduce a simple technique for reducing the size of conventional indexes on such highly repetitive texts. Given upper bounds on pattern lengths and edit distances, we pre-process the text with the lossless data compression algorithm LZ77 to obtain a filtered text, for which we store a conventional index. Later, given a query, we find all matches in the filtered text, then use their positions and the structure of the LZ77 parse to find all matches in the original text. Our experiments show that this also significantly reduces query times.

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