Improving Data Warehouse Performance Using Filesystem Technology with GZIP, LZJB and ZLE Compression Andre Wenas1, Suharjito2
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Data Compression for Remote Laboratories
Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. One of the main benefits of online laboratories is the possibility of using fewer re- sources to serve more people i.e. -
Nxadmin CLI Reference Guide Unity Iv Contents
HYPER-UNIFIED STORAGE nxadmin Command Line Interface Reference Guide NEXSAN | 325 E. Hillcrest Drive, Suite #150 | Thousand Oaks, CA 91360 USA Printed Thursday, July 26, 2018 | www.nexsan.com Copyright © 2010—2018 Nexsan Technologies, Inc. All rights reserved. Trademarks Nexsan® is a trademark or registered trademark of Nexsan Technologies, Inc. The Nexsan logo is a registered trademark of Nexsan Technologies, Inc. All other trademarks and registered trademarks are the property of their respective owners. Patents This product is protected by one or more of the following patents, and other pending patent applications worldwide: United States patents US8,191,841, US8,120,922; United Kingdom patents GB2466535B, GB2467622B, GB2467404B, GB2296798B, GB2297636B About this document Unauthorized use, duplication, or modification of this document in whole or in part without the written consent of Nexsan Corporation is strictly prohibited. Nexsan Technologies, Inc. reserves the right to make changes to this manual, as well as the equipment and software described in this manual, at any time without notice. This manual may contain links to web sites that were current at the time of publication, but have since been moved or become inactive. It may also contain links to sites owned and operated by third parties. Nexsan is not responsible for the content of any such third-party site. Contents Contents Contents iii Chapter 1: Accessing the nxadmin and nxcmd CLIs 15 Connecting to the Unity Storage System using SSH 15 Prerequisite 15 Connecting to the Unity -
Smaller Flight Data Recorders{
Available online at http://docs.lib.purdue.edu/jate Journal of Aviation Technology and Engineering 2:2 (2013) 45–55 Smaller Flight Data Recorders{ Yair Wiseman and Alon Barkai Bar-Ilan University Abstract Data captured by flight data recorders are generally stored on the system’s embedded hard disk. A common problem is the lack of storage space on the disk. This lack of space for data storage leads to either a constant effort to reduce the space used by data, or to increased costs due to acquisition of additional space, which is not always possible. File compression can solve the problem, but carries with it the potential drawback of the increased overhead required when writing the data to the disk, putting an excessive load on the system and degrading system performance. The author suggests the use of an efficient compressed file system that both compresses data in real time and ensures that there will be minimal impact on the performance of other tasks. Keywords: flight data recorder, data compression, file system Introduction A flight data recorder is a small line-replaceable computer unit employed in aircraft. Its function is recording pilots’ inputs, electronic inputs, sensor positions and instructions sent to any electronic systems on the aircraft. It is unofficially referred to as a "black box". Flight data recorders are designed to be small and thoroughly fabricated to withstand the influence of high speed impacts and extreme temperatures. A flight data recorder from a commercial aircraft can be seen in Figure 1. State-of-the-art high density flash memory devices have permitted the solid state flight data recorder (SSFDR) to be implemented with much larger memory capacity. -
The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed. -
The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
sensors Article The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities Ammar Nasif *, Zulaiha Ali Othman and Nor Samsiah Sani Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia; [email protected] (Z.A.O.); [email protected] (N.S.S.) * Correspondence: [email protected] Abstract: Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, Citation: Nasif, A.; Othman, Z.A.; we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Sani, N.S. -
The Illusion of Space and the Science of Data Compression And
The Illusion of Space and the Science of Data Compression and Deduplication Bruce Yellin EMC Proven Professional Knowledge Sharing 2010 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents What Was Old Is New Again ......................................................................................................... 4 The Business Benefits of Saving Space ....................................................................................... 6 Data Compression Strategies ....................................................................................................... 9 Data Compression Basics ....................................................................................................... 10 Compression Bakeoff .............................................................................................................. 13 Data Deduplication Strategies .................................................................................................... 16 Deduplication - Theory of Operation ....................................................................................... 16 File Level Deduplication - Single Instance Storage ................................................................. 21 Fixed-Block Deduplication ....................................................................................................... 23 Variable-Block Deduplication .................................................................................................. 24 Content-Aware Deduplication -
Data Compression for Remote Laboratories
CORE Metadata, citation and similar papers at core.ac.uk Provided by Online-Journals.org (International Association of Online Engineering) Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. -
Compression: Putting the Squeeze on Storage
Compression: Putting the Squeeze on Storage Live Webcast September 2, 2020 11:00 am PT 1 | ©2020 Storage Networking Industry Association. All Rights Reserved. Today’s Presenters Ilker Cebeli John Kim Brian Will Moderator Chair, SNIA Networking Storage Forum Intel® QuickAssist Technology Samsung NVIDIA Software Architect Intel 2 | ©2020 Storage Networking Industry Association. All Rights Reserved. SNIA-At-A-Glance 3 3 | ©2020 Storage Networking Industry Association. All Rights Reserved. NSF Technologies 4 4 | ©2020 Storage Networking Industry Association. All Rights Reserved. SNIA Legal Notice § The material contained in this presentation is copyrighted by the SNIA unless otherwise noted. § Member companies and individual members may use this material in presentations and literature under the following conditions: § Any slide or slides used must be reproduced in their entirety without modification § The SNIA must be acknowledged as the source of any material used in the body of any document containing material from these presentations. § This presentation is a project of the SNIA. § Neither the author nor the presenter is an attorney and nothing in this presentation is intended to be, or should be construed as legal advice or an opinion of counsel. If you need legal advice or a legal opinion please contact your attorney. § The information presented herein represents the author's personal opinion and current understanding of the relevant issues involved. The author, the presenter, and the SNIA do not assume any responsibility or liability for damages arising out of any reliance on or use of this information. NO WARRANTIES, EXPRESS OR IMPLIED. USE AT YOUR OWN RISK. 5 | ©2020 Storage Networking Industry Association. -
Accelerating Compressed File Pattern Matching with Parabix Techniques
Accelerating Compressed File Pattern Matching with Parabix Techniques by Xiangyu Wu B.Eng., Tongji University, China, 2015 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the School of Computing Science Faculty of Applied Sciences © Xiangyu Wu 2018 SIMON FRASER UNIVERSITY Fall 2018 All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced without authorization under the conditions for "Fair Dealing". Therefore, limited reproduction of this work for the purposes of private study, research, education, satire, parody, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. Approval Name: Xiangyu Wu Degree: Master of Science Title: Accelerating Compressed File Pattern Matching with Parabix Techniques Examining Committee: Chair: Kay Wiese Associate Professor Robert Cameron Senior Supervisor Professor William Sumner Supervisor Assistant Professor Fred Popowich Examiner Professor Date Defended: October 5, 2018 ii Abstract Data compression is a commonly used technique in the big data environment. On the other hand, efficient information retrieval of those compressed big data is also a standard requirement. In this thesis, we proposed a new method of compressed file pattern matching inspired by the bitstream pattern matching approach from ICGrep, a high- performance regular expression matching tool based on Parabix techniques. Instead of using the traditional way that fully decompresses the compressed file before pattern matching, our approach handles many complex procedures in the small compressed space. We selected LZ4 as a sample compression format and implemented a compressed file pattern matching tool LZ4 Grep, which showed small performance improvement. -
1 Introduction
HELSINKI UNIVERSITY OF TECHNOLOGY Faculty of Electronics, Communications, and Automation Department of Communications and Networking Le Wang Evaluation of Compression for Energy-aware Communication in Wireless Networks Master’s Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Technology. Espoo, May 11, 2009 Supervisor: Professor Jukka Manner Instructor: Sebastian Siikavirta 2 HELSINKI UNIVERSITY OF TECHNOLOGY ABSTRACT OF MASTER’S THESIS Author: Le Wang Title: Evaluation of Compression for Energy-aware Communication in Wireless Networks Number of pages: 75 p. Date: 11th May 2009 Faculty: Faculty of Electronics, Communications, and Automation Department: Department of Communications and Networks Code: S-38 Supervisor: Professor Jukka Manner Instructor: Sebastian Siikavirta Abstract In accordance with the development of ICT-based communication, energy efficient communication in wireless networks is being required for reducing energy consumption, cutting down greenhouse emissions and improving business competitiveness. Due to significant energy consumption of transmitting data over wireless networks, data compression techniques can be used to trade the overhead of compression/decompression for less communication energy. Careless and blind compression in wireless networks not only causes an expansion of file sizes, but also wastes energy. This study aims to investigate the usages of data compression to reduce the energy consumption in a hand-held device. By con- ducting experiments as the methodologies, the impacts of transmission on energy consumption are explored on wireless interfaces. Then, 9 lossless compression algo- rithms are examined on popular Internet traffic in the view of compression ratio, speed and consumed energy. Additionally, energy consumption of uplink, downlink and overall system is investigated to achieve a comprehensive understanding of compression in wireless networks. -
Data Compression in Solid State Storage
Data Compression in Solid State Storage John Fryar [email protected] Santa Clara, CA August 2013 1 Acknowledgements This presentation would not have been possible without the counsel, hard work and graciousness of the following individuals and/or organizations: Raymond Savarda Sandgate Technologies Santa Clara, CA August 2013 2 Disclaimers The opinions expressed herein are those of the author and do not necessarily represent those of any other organization or individual unless specifically cited. A thorough attempt to acknowledge all sources has been made. That said, we’re all human… Santa Clara, CA August 2013 3 Learning Objectives At the conclusion of this tutorial the audience will have been exposed to: • The different types of Data Compression • Common Data Compression Algorithms • The Deflate/Inflate (GZIP/GUNZIP) algorithms in detail • Implementation Options (Software/Hardware) • Impacts of design parameters in Performance • SSD benefits and challenges • Resources for Further Study Santa Clara, CA August 2013 4 Agenda • Background, Definitions, & Context • Data Compression Overview • Data Compression Algorithm Survey • Deflate/Inflate (GZIP/GUNZIP) in depth • Software Implementations • HW Implementations • Tradeoffs & Advanced Topics • SSD Benefits and Challenges • Conclusions Santa Clara, CA August 2013 5 Definitions Item Description Comments Open A system which will compress Must strictly adhere to standards System data for use by other entities. on compress / decompress I.E. the compressed data will algorithms exit the system Interoperability among vendors mandated for Open Systems Closed A system which utilizes Can support a limited, optimized System compressed data internally but subset of standard. does not expose compressed Also allows custom algorithms data to the outside world No Interoperability req’d. -
Better Adaptive Text Compression Scheme
Journal of Education and Science Vol. 27, No.2, 2018, pp. 48-57 ISSN 1812-125X http://edusj.mosuljournals.com Better Adaptive Text Compression Scheme Duha Amir Sultan Dept. of Biology/ Education College of Girls/ University of Mosul تاريخ اﻻستﻻم 17/04/2012 تاريخ القبول 2012/11/07 الخﻻصة ظهرت طر ق عدي دي لكبس البيدااداتو حاةدي أش ر ددددددددددددددهر طديق اللرق ر ط اللريقد ال قترة أش قبل العال ي ش Ziv ح Lempel حالت رطلق عليها اسددددددددد LZ77و ث طورت طيق اللريق أش قبددل العددال يش Storer و Szymanski لتظهر طريقدد يدديدددي ط LZSS و حالت رعلددن اتددا رفضدل أقارا ساداسقاتها سعي رط طبقن عل اصدو ثيير حخدة ف طاتيش اللريقتيش حي ي ع اللرق اﻷخرى الت تنضوي تحن تصنيف اللرق اليدناأيك ي لكبس البيااات دت أاح ال لف أش اليادددار ل الي يش اد اط رطول تلابق بيش أةزط البيااا ت )الن ص ال شدددمر أادددبقاص حالن ص اليي سدديشددمر اللريق ال قترة ف طيا البحث تعت ي أبير البح ث سإت اطيشو أش الياددار ل الي يش ث أش الي يش ل اليادددددددددددددارو عل الرر رط طيق اللريق تحتان حقتا رطول اوعا أا و ﻻ راها رعل ن اتي ثبس اكبر للبيااات Abstract A data compression scheme suggested by Ziv and Lempel, LZ77, is applied to text compression. A slightly modified version suggested by Storer and Szymanski ,LZSS, is found to achieve compression ratios as good as most existing schemes for a wide range of texts. In these two methods and all other dynamic methods, a text file is searched from left to right to find the longest match between the lookahead buffer (the previously encoded text) and the characters to be encoded.