Compresso: Efficient Compression of Segmentation Data for Connectomics
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Package 'Brotli'
Package ‘brotli’ May 13, 2018 Type Package Title A Compression Format Optimized for the Web Version 1.2 Description A lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip) but offers more dense compression. License MIT + file LICENSE URL https://tools.ietf.org/html/rfc7932 (spec) https://github.com/google/brotli#readme (upstream) http://github.com/jeroen/brotli#read (devel) BugReports http://github.com/jeroen/brotli/issues VignetteBuilder knitr, R.rsp Suggests spelling, knitr, R.rsp, microbenchmark, rmarkdown, ggplot2 RoxygenNote 6.0.1 Language en-US NeedsCompilation yes Author Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), Google, Inc [aut, cph] (Brotli C++ library) Maintainer Jeroen Ooms <[email protected]> Repository CRAN Date/Publication 2018-05-13 20:31:43 UTC R topics documented: brotli . .2 Index 4 1 2 brotli brotli Brotli Compression Description Brotli is a compression algorithm optimized for the web, in particular small text documents. Usage brotli_compress(buf, quality = 11, window = 22) brotli_decompress(buf) Arguments buf raw vector with data to compress/decompress quality value between 0 and 11 window log of window size Details Brotli decompression is at least as fast as for gzip while significantly improving the compression ratio. The price we pay is that compression is much slower than gzip. Brotli is therefore most effective for serving static content such as fonts and html pages. For binary (non-text) data, the compression ratio of Brotli usually does not beat bz2 or xz (lzma), however decompression for these algorithms is too slow for browsers in e.g. -
ROOT I/O Compression Improvements for HEP Analysis
EPJ Web of Conferences 245, 02017 (2020) https://doi.org/10.1051/epjconf/202024502017 CHEP 2019 ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and interaction with other data analysis ecosys- tems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased over the last couple of years. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly, because there are significant trade-offs between the increased CPU cost for reading and writing files and the reduces storage space. 1 Introduction In the past years, Large Hadron Collider (LHC) experiments are managing about an exabyte of storage for analysis purposes, approximately half of which is stored on tape storages for archival purposes, and half is used for traditional disk storage. Meanwhile for High Lumi- nosity Large Hadron Collider (HL-LHC) storage requirements per year are expected to be increased by a factor of 10 [1]. -
Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing. -
A Novel Coding Architecture for Multi-Line Lidar Point Clouds
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network Xuebin Sun , Sukai Wang , Graduate Student Member, IEEE, and Ming Liu , Senior Member, IEEE Abstract— Light detection and ranging (LiDAR) plays an preservation of historical relics, 3D sensing for smart city, indispensable role in autonomous driving technologies, such as well as autonomous driving. Especially for autonomous as localization, map building, navigation and object avoidance. driving systems, LiDAR sensors play an indispensable role However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, in a large number of key techniques, such as simultaneous we propose a novel compression architecture for multi-line localization and mapping (SLAM) [1], path planning [2], LiDAR point cloud sequences based on clustering and convolu- obstacle avoidance [3], and navigation. A point cloud consists tional long short-term memory (LSTM) networks. LiDAR point of a set of individual 3D points, in accordance with one or clouds are structured, which provides an opportunity to convert more attributes (color, reflectance, surface normal, etc). For the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image instance, the Velodyne HDL-64E LiDAR sensor generates a sequence compression problem. Inspired by the high efficiency point cloud of up to 2.2 billion points per second, with a video coding (HEVC) algorithm, we design a novel compression range of up to 120 m. -
Unfoldr Dstep
Asymmetric Numeral Systems Jeremy Gibbons WG2.11#19 Salem ANS 2 1. Coding Huffman coding (HC) • efficient; optimally effective for bit-sequence-per-symbol arithmetic coding (AC) • Shannon-optimal (fractional entropy); but computationally expensive asymmetric numeral systems (ANS) • efficiency of Huffman, effectiveness of arithmetic coding applications of streaming (another story) • ANS introduced by Jarek Duda (2006–2013). Now: Facebook (Zstandard), Apple (LZFSE), Google (Draco), Dropbox (DivANS). ANS 3 2. Intervals Pairs of rationals type Interval (Rational, Rational) = with operations unit (0, 1) = weight (l, r) x l (r l) x = + − ⇥ narrow i (p, q) (weight i p, weight i q) = scale (l, r) x (x l)/(r l) = − − widen i (p, q) (scale i p, scale i q) = so that narrow and unit form a monoid, and inverse relationships: weight i x i x unit 2 () 2 weight i x y scale i y x = () = narrow i j k widen i k j = () = ANS 4 3. Models Given counts :: [(Symbol, Integer)] get encodeSym :: Symbol Interval ! decodeSym :: Rational Symbol ! such that decodeSym x s x encodeSym s = () 2 1 1 1 1 Eg alphabet ‘a’, ‘b’, ‘c’ with counts 2, 3, 5 encoded as (0, /5), ( /5, /2), and ( /2, 1). { } ANS 5 4. Arithmetic coding encode1 :: [Symbol ] Rational ! encode1 pick foldl estep unit where = ◦ 1 estep :: Interval Symbol Interval 1 ! ! estep is narrow i (encodeSym s) 1 = decode1 :: Rational [Symbol ] ! decode1 unfoldr dstep where = 1 dstep :: Rational Maybe (Symbol, Rational) 1 ! dstep x let s decodeSym x in Just (s, scale (encodeSym s) x) 1 = = where pick :: Interval Rational satisfies pick i i. -
Data Latency and Compression
Data Latency and Compression Joseph M. Steim1, Edelvays N. Spassov2 1Quanterra Inc., 2Kinemetrics, Inc. Abstract DIscussion More Compression Because of interest in the capability of digital seismic data systems to provide low-latency data for “Early Compression and Latency related to Origin Queuing[5] Lossless Lempel-Ziv (LZ) compression methods (e.g. gzip, 7zip, and more recently “brotli” introduced by Warning” applications, we have examined the effect of data compression on the ability of systems to Plausible data compression methods reduce the volume (number of bits) required to represent each data Google for web-page compression) are often used for lossless compression of binary and text files. How deliver data with low latency, and on the efficiency of data storage and telemetry systems. Quanterra Q330 sample. Compression of data therefore always reduces the minimum required bandwidth (bits/sec) for does the best example of modern LZ methods compare with FDSN Level 1 and 2 for waveform data? systems are widely used in telemetered networks, and are considered in particular. transmission compared with transmission of uncompressed data. The volume required to transmit Segments of data representing up to one month of various levels of compressibility were compressed using compressed data is variable, depending on characteristics of the data. Higher levels of compression require Levels 1,2 and 3, and brotli using “quality” 1 and 6. The compression ratio (relative to the original 32 bits) Q330 data acquisition systems transmit data compressed in Steim2-format packets. Some studies have less minimum bandwidth and volume, and maximize the available unused (“free”) bandwidth for shows that in all cases the compression using L1,L2, or L3 is significantly greater than brotli. -
Parquet Data Format Performance
Parquet data format performance Jim Pivarski Princeton University { DIANA-HEP February 21, 2018 1 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 Developed independently to do the same thing Google Dremel authors claimed to be unaware of any precedents, so this is an example of convergent evolution. -
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. -
Key-Based Self-Driven Compression in Columnar Binary JSON
Otto von Guericke University of Magdeburg Department of Computer Science Master's Thesis Key-Based Self-Driven Compression in Columnar Binary JSON Author: Oskar Kirmis November 4, 2019 Advisors: Prof. Dr. rer. nat. habil. Gunter Saake M. Sc. Marcus Pinnecke Institute for Technical and Business Information Systems / Database Research Group Kirmis, Oskar: Key-Based Self-Driven Compression in Columnar Binary JSON Master's Thesis, Otto von Guericke University of Magdeburg, 2019 Abstract A large part of the data that is available today in organizations or publicly is provided in semi-structured form. To perform analytical tasks on these { mostly read-only { semi-structured datasets, Carbon archives were developed as a column-oriented storage format. Its main focus is to allow cache-efficient access to fields across records. As many semi-structured datasets mainly consist of string data and the denormalization introduces redundancy, a lot of storage space is required. However, in Carbon archives { besides a deduplication of strings { there is currently no compression implemented. The goal of this thesis is to discuss, implement and evaluate suitable compression tech- niques to reduce the amount of storage required and to speed up analytical queries on Carbon archives. Therefore, a compressor is implemented that can be configured to apply a combination of up to three different compression algorithms to the string data of Carbon archives. This compressor can be applied with a different configuration per column (per JSON object key). To find suitable combinations of compression algo- rithms for each column, one manual and two self-driven approaches are implemented and evaluated. On a set of ten publicly available semi-structured datasets of different kinds and sizes, the string data can be compressed down to about 53% on average, reducing the whole datasets' size by 20%. -
Session Overhead Reduction in Adaptive Streaming
Session Overhead Reduction in Adaptive Streaming A Technical Paper prepared for SCTE•ISBE by Alexander Giladi Fellow Comcast 1899 Wynkoop St., Denver CO +1 (215) 581-7118 [email protected] © 2020 SCTE•ISBE and NCTA. All rights reserved. 1 Table of Contents Title Page Number 1. Introduction .......................................................................................................................................... 3 2. Session overhead and HTTP compression ........................................................................................ 4 3. Reducing traffic overhead ................................................................................................................... 5 3.1. MPD patch .............................................................................................................................. 6 3.2. Segment Gap Signaling ......................................................................................................... 7 3.3. Efficient multi-DRM signaling ................................................................................................. 8 4. Reducing the number of HTTP requests ............................................................................................ 9 4.1. Asynchronous MPD updates.................................................................................................. 9 4.2. Predictive templates ............................................................................................................. 10 4.3. Timeline extension .............................................................................................................. -
Forcepoint DLP Supported File Formats and Size Limits
Forcepoint DLP Supported File Formats and Size Limits Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 This article provides a list of the file formats that can be analyzed by Forcepoint DLP, file formats from which content and meta data can be extracted, and the file size limits for network, endpoint, and discovery functions. See: ● Supported File Formats ● File Size Limits © 2021 Forcepoint LLC Supported File Formats Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 The following tables lists the file formats supported by Forcepoint DLP. File formats are in alphabetical order by format group. ● Archive For mats, page 3 ● Backup Formats, page 7 ● Business Intelligence (BI) and Analysis Formats, page 8 ● Computer-Aided Design Formats, page 9 ● Cryptography Formats, page 12 ● Database Formats, page 14 ● Desktop publishing formats, page 16 ● eBook/Audio book formats, page 17 ● Executable formats, page 18 ● Font formats, page 20 ● Graphics formats - general, page 21 ● Graphics formats - vector graphics, page 26 ● Library formats, page 29 ● Log formats, page 30 ● Mail formats, page 31 ● Multimedia formats, page 32 ● Object formats, page 37 ● Presentation formats, page 38 ● Project management formats, page 40 ● Spreadsheet formats, page 41 ● Text and markup formats, page 43 ● Word processing formats, page 45 ● Miscellaneous formats, page 53 Supported file formats are added and updated frequently. Key to support tables Symbol Description Y The format is supported N The format is not supported P Partial metadata -
Context-Aware Encoding & Delivery in The
Context-Aware Encoding & Delivery in the Web ICWE 2020 Benjamin Wollmer, Wolfram Wingerath, Norbert Ritter Universität Hamburg 9 - 12 June, 2020 Business Impact of Page Speed Business Uplift Speed Speed Downlift Uplift Business Downlift Felix Gessert: Mobile Site Speed and the Impact on E-Commerce, CodeTalks 2019 So Far On Compression… GZip SDCH Deflate Delta Brotli Encoding GZIP/Deflate – The De Facto Standard in the Web Encoding Size None 200 kB Gzip ~36 kB This example text is used to show how LZ77 finds repeating elements in the example[70;14] text ~81.9% saved data J. Alakuijala, E. Kliuchnikov, Z. Szabadka, L. Vandevenne: Comparison of Brotli, Deflate, Zopfli, LZMA, LZHAM and Bzip2 Compression Algorithms, 2015 Delta Encoding – Updating Stale Content Encoding Size None 200 kB Gzip ~36 kB Delta Encoding ~34 kB 83% saved data J. C. Mogul, F. Douglis, A. Feldmann, B. Krishnamurthy: Potential Benefits of Delta Encoding and Data Compression for HTTP, 1997 SDCH – Reusing Dictionaries Encoding Size This is an example None 200 kB Gzip ~36 kB Another example Delta Encoding ~34 kB SDCH ~7 kB Up to 81% better results (compared to gzip) O. Shapira: Shared Dictionary Compression for HTTP at LinkedIn, 2015 Brotli – SDCH for Everyone Encoding Size None 200 kB Gzip ~36 kB Delta Encoding ~34 kB SDCH ~7 kB Brotli ~29 kB ~85.6% saved data J. Alakuijala, E. Kliuchnikov, Z. Szabadka, L. Vandevenne: Comparison of Brotli, Deflate, Zopfli, LZMA, LZHAM and Bzip2 Compression Algorithms, 2015 So Far On Compression… Theory vs. Reality GZip (~80%) SDCH Deflate