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Announcement DC5m United States software in english 1 articles, created at 2016-12-25 16:00 articles set mostly positive rate 10.0 1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. 2016-12-25 05:00 1KB www.infoq.com Articles DC5m United States software in english 1 articles, created at 2016-12-25 16:00 1 /1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. Brotli is on by default in the latest Edge build and can be previewed via the Windows Insider Program. It will reach stable status early next year, says Microsoft. Microsoft touts a 20% higher compression ratios over comparable compression algorithms, which would benefit page load times without impacting client-side CPU costs. According to Google, Brotli uses a whole new data format , which makes it incompatible with Deflate but ensures higher compression ratios. In particular, Google says, Brotli is roughly as fast as zlib when decompressing and provides a better compression ratio than LZMA and bzip2 on the Canterbury Corpus. Brotli appears to be especially tuned for the web , that is for offline encoding and online decoding of Web assets, or Android APKs. Google claims a compression ratio improvement of 20–26% over its own Zopfli algorithm, which still provides the best compression ratio of any deflate algorithm. Last year, Apple and Facebook both open-sourced their new compression algorithms, Apple’s LZFSE and Facebooks’s ZStandard. By being included in the Edge browser, Brotli is now supported by all the major browsers, including Chrome and Firefox, while it is still unsupported in Safari and Internet Explorer 11. 2016-12-25 05:00 Sergio De www.infoq.com Total 1 articles. Items detected: 1, scanned: 1, accumulated: 1, inserted: 1, empty media: 0, not matched limits: 1, skipped: {total: 0, by unique value: 0, by limits: 0, by similarity: 0, by unicity: 0, dates: 0, by classifier: 0, by blacklist: 0, by mandatory tag: 0}, bad dates: 0, similar from same domain: 0; the same images URLs found 1 times; total 1 languages detected: {u'en': 3}; {u'text': {u'chars': 98559, u'bytes': 396108, u'words': 10232, u'sentences': 19}} Created at 2016-12-25 16:00.

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I Introduction
PPM Performance with BWT Complexity: A New Metho d for Lossless Data Compression Michelle E ros California Institute of Technology e [email protected] Abstract This work combines a new fast context-search algorithm with the lossless source co ding mo dels of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv-Lemp el co des and the compression p erformance of PPM-based algorithms. Both se- quential and nonsequential enco ding are considered. The prop osed algorithm yields an average rate of 2.27 bits per character bp c on the Calgary corpus, comparing favorably to the 2.33 and 2.34 bp c of PPM5 and PPM and the 2.43 bp c of BW94 but not matching the 2.12 bp c of PPMZ9, which, at the time of this publication, gives the greatest compression of all algorithms rep orted on the Calgary corpus results page. The prop osed algorithm gives an average rate of 2.14 bp c on the Canterbury corpus. The Canterbury corpus web page gives average rates of 1.99 bp c for PPMZ9, 2.11 bp c for PPM5, 2.15 bp c for PPM7, and 2.23 bp c for BZIP2 a BWT-based co de on the same data set. I Intro duction The Burrows Wheeler Transform BWT [1] is a reversible sequence transformation that is b ecoming increasingly p opular for lossless data compression. The BWT rear- ranges the symb ols of a data sequence in order to group together all symb ols that share the same unb ounded history or \context." Intuitively, this op eration is achieved by forming a table in which each row is a distinct cyclic shift of the original data string.
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Implementing Associative Coder of Buyanovsky (ACB)
Implementing Associative Coder of Buyanovsky (ACB) data compression by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Montana State University © Copyright by Sean Michael Lambert (1999) Abstract: In 1994 George Mechislavovich Buyanovsky published a basic description of a new data compression algorithm he called the “Associative Coder of Buyanovsky,” or ACB. The archive program using this idea, which he released in 1996 and updated in 1997, is still one of the best general compression utilities available. Despite this, the ACB algorithm is still barely understood by data compression experts, primarily because Buyanovsky never published a detailed description of it. ACB is a new idea in data compression, merging concepts from existing statistical and dictionary-based algorithms with entirely original ideas. This document presents several variations of the ACB algorithm and the details required to implement a basic version of ACB. IMPLEMENTING ASSOCIATIVE CODER OF BUYANOVSKY (ACB) DATA COMPRESSION by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science MONTANA STATE UNIVERSITY-BOZEMAN Bozeman, Montana April 1999 © COPYRIGHT by Sean Michael Lambert 1999 All Rights Reserved ii APPROVAL of a thesis submitted by Sean Michael Lambert This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the College of Graduate Studies. Brendan Mumey U /l! ^ (Signature) Date Approved for the Department of Computer Science J.
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An Analysis of XML Compression Efficiency
An Analysis of XML Compression Efficiency Christopher J. Augeri1 Barry E. Mullins1 Leemon C. Baird III Dursun A. Bulutoglu2 Rusty O. Baldwin1 1Department of Electrical and Computer Engineering Department of Computer Science 2Department of Mathematics and Statistics United States Air Force Academy (USAFA) Air Force Institute of Technology (AFIT) USAFA, Colorado Springs, CO Wright Patterson Air Force Base, Dayton, OH {chris.augeri, barry.mullins}@afit.edu [email protected] {dursun.bulutoglu, rusty.baldwin}@afit.edu ABSTRACT We expand previous XML compression studies [9, 26, 34, 47] by XML simplifies data exchange among heterogeneous computers, proposing the XML file corpus and a combined efficiency metric. but it is notoriously verbose and has spawned the development of The corpus was assembled using guidelines given by developers many XML-specific compressors and binary formats. We present of the Canterbury corpus [3], files often used to assess compressor an XML test corpus and a combined efficiency metric integrating performance. The efficiency metric combines execution speed compression ratio and execution speed. We use this corpus and and compression ratio, enabling simultaneous assessment of these linear regression to assess 14 general-purpose and XML-specific metrics, versus prioritizing one metric over the other. We analyze compressors relative to the proposed metric. We also identify key collected metrics using linear regression models (ANOVA) versus factors when selecting a compressor. Our results show XMill or a simple comparison of means, e.g., X is 20% better than Y. WBXML may be useful in some instances, but a general-purpose compressor is often the best choice. 2. XML OVERVIEW XML has gained much acceptance since first proposed in 1998 by Categories and Subject Descriptors the World-Wide Web Consortium (W3C).
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Improving Compression-Ratio in Backup
Institutionen för systemteknik Department of Electrical Engineering Examensarbete Improving compression ratio in backup Examensarbete utfört i Informationskodning/Bildkodning av Mattias Zeidlitz Författare Mattias Zeidlitz LITH-ISY-EX--12/4588--SE Linköping 2012 TEKNISKA HÖGSKOLAN LINKÖPINGS UNIVERSITET Department of Electrical Engineering Linköpings tekniska högskola Linköping University Institutionen för systemteknik S-581 83 Linköping, Sweden 581 83 Linköping Improving compression-ratio in backup ............................................................................ Examensarbete utfört i Informationskodning/Bildkodning vid Linköpings tekniska högskola av Mattias Zeidlitz ............................................................. LITH-ISY-EX--12/4588--SE Presentationsdatum Institution och avdelning 2012-06-13 Institutionen för systemteknik Publiceringsdatum (elektronisk version) Department of Electrical Engineering Datum då du ämnar publicera exjobbet Språk Typ av publikation ISBN (licentiatavhandling) Svenska Licentiatavhandling ISRN LITH-ISY-EX--12/4588--SE x Annat (ange nedan) x Examensarbete Serietitel (licentiatavhandling) C-uppsats D-uppsats Engelska Rapport Serienummer/ISSN (licentiatavhandling) Antal sidor Annat (ange nedan) 58 URL för elektronisk version http://www.ep.liu.se Publikationens titel Improving compression ratio in backup Författare Mattias Zeidlitz Sammanfattning Denna rapport beskriver ett examensarbete genomfört på Degoo Backup AB i Stockholm under våren 2012. Syftet var att designa en kompressionssvit
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Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice
Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individuals may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced without modification The SNIA must be acknowledged as source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 2 © 2007 Storage Networking Industry Association. All Rights Reserved. Abstract Benefits of Hardware Data Compression in Storage Networks This tutorial explains the benefits and algorithmic details of lossless data compression in Storage Networks, and focuses especially on data de-duplication. The material presents a brief history and background of Data Compression - a primer on the different data compression algorithms in use today. This primer includes performance data on the specific compression algorithms, as well as performance on different data types. Participants will come away with a good understanding of where to place compression in the data path, and the benefits to be gained by such placement. The tutorial will discuss technological advances in compression and how they affect system level solutions. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 3 © 2007 Storage Networking Industry Association. All Rights Reserved. Agenda Introduction and Background Lossless Compression Algorithms System Implementations Technology Advances and Compression Hardware Power Conservation and Efficiency Conclusion Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 4 © 2007 Storage Networking Industry Association.
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The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
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International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected] Volume 3, Issue 6, June 2014 ISSN 2319 - 4847 Evaluation of Dominant Text Data Compression Techniques 1Somefun, Olawale M. 2Adebayo, Adewale O. 1,2School of Computing and Engineering Sciences, Babcock University, Ilisan-Remo, Ogun, Nigeria Abstract Compression refers to reducing the quantity of data, bits used to represent, store and/or transmit file content, without excessively reducing the quality of the original data. Data Compression technique can be Lossless which enables exact reproduction of the original data on decomposition or Lossy which does not. Text data compression techniques are normally lossless. There are many text data compression algorithms, and it is essential that best ones be identified and evaluated towards optimizing compression purposes. This research focused on proffering key compression algorithms, and evaluating them towards ensuring selection of better algorithms over poorer ones. Dominant text data compression algorithms employing Statistical and Dictionary based compression techniques were determined through qualitative analysis of extant literature for convergence and expositions using inductive approach. The proffered algorithms were implemented in Java, and were evaluated along compression ratio, compression and decompression time using text files obtained from Canterbury corpus. Huffman and Arithmetic coding techniques were proffered for statistical, and Lempel-Ziv-Welch (LZW) for dictionary-based technique. LZW was indicated the best with the highest compression ratio of 2.36314, followed by Arithmetic with compression ratio of 1.70534, and Huffman with compression ratio of 1.62877. It was noted that the performance of the data compression algorithms on compression time and decompression time depend on the characteristics of the files, the different symbols and symbol frequencies.
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On Prediction Using Variable Order Markov Models
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The Illusion of Space and the Science of Data Compression And
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Modern Lossless Compression Techniques: Review, Comparison and Analysis
JAC : A Journal Of Composition Theory ISSN : 0731-6755 Modern Lossless Compression Techniques: Review, Comparison and Analysis B ASHA KIRAN, M MOUNIKA,B L SRINIVAS Dept of EEE , MRIET. Abstract— With the world drifting more and more enables its storage and transmission over a limited towards the social network, the size and amount of bandwidth channel. Fig.1 shows basic components of a data shared over the internet is increasing day by day. data compression system. The input data is processed by a Since the network bandwidth is always limited, we Data Compressor, which usually iterates over the data require efficient compression algorithms to facilitate twice. In the first iteration, the compression algorithm fast and efficient sharing of data over the network. In tries to gain knowledge about the data which in turn is this paper, we discuss algorithms of widely used used for efficient compression in the second iteration. The traditional and modern compression techniques. In an compressed data along with the additional data used for effort to find the optimum compression algorithm, we efficient compression is then stored or transmitted through compare commonly used modern compression a network to the receiver. The receiver then decompresses algorithms: Deflate, Bzip2, LZMA, PPMd and the data using a decompression algorithm. Though data PPMonstr by analyzing their performance on Silesia compression can reduce the bandwidth usage of a network corpus. The corpus comprises of files of varied type and the storage space, it requires additional computational and sizes, which accurately simulates the vast diversity resources for compression and decompression of data, and of files shared over the internet.
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