DOCSLIB.ORG

Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data Real-Time Analytics Techniques to Analyze and Visualize Streaming Data Byron Ellis ffi rs.indd 11:51:43:AM 06/06/2014 Page i Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data Published by John Wiley & Sons, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www.wiley.com Copyright © 2014 by John Wiley & Sons, Inc., Indianapolis, Indiana Published simultaneously in Canada ISBN: 978-1-118-83791-7 ISBN: 978-1-118-83793-1 (ebk) ISBN: 978-1-118-83802-0 (ebk) Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permis- sion of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley. com/go/permissions. Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or war- ranties with respect to the accuracy or completeness of the contents of this work and specifi cally disclaim all warranties, including without limitation warranties of fi tness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites listed in this work may have changed or disappeared between when this work was written and when it is read. For general information on our other products and services please contact our Customer Care Department within the United States at (877) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Library of Congress Control Number: 2014935749 Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affi liates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book. ffi rs.indd 11:51:43:AM 06/06/2014 Page ii As always, for Natasha. ffi rs.indd 11:51:43:AM 06/06/2014 Page iii Credits Executive Editor Marketing Manager Robert Elliott Carrie Sherrill Project Editor Business Manager Kelly Talbot Amy Knies Technical Editors Vice President and Executive Luke Hornof Group Publisher Ben Peirce Richard Swadley Jose Quinteiro Associate Publisher Production Editors Jim Minatel Christine Mugnolo Project Coordinator, Cover Daniel Scribner Todd Klemme Copy Editor Proofreader Charlotte Kugen Nancy Carrasco Manager of Content Development Indexer and Assembly John Sleeva Mary Beth Wakefi eld Cover Designer Director of Community Marketing Wiley David Mayhew iv ffi rs.indd 11:51:43:AM 06/06/2014 Page iv About the Author Byron Ellis is the CTO of Spongecell, an advertising technology fi rm based in New York, with offi ces in San Francisco, Chicago, and London. He is responsible for research and development as well as the maintenance of Spongecell’s com- puting infrastructure. Prior to joining Spongecell, he was Chief Data Scientist for Liveperson, a leading provider of online engagement technology. He also held a variety of positions at adBrite, Inc, one of the world’s largest advertising exchanges at the time. Additionally, he has a PhD in Statistics from Harvard where he studied methods for learning the structure of networks from experi- mental data obtained from high throughput biology experiments. About the Technical Editors With 20 years of technology experience, Jose Quinteiro has been an integral part of the design and development of a signifi cant number of end-user, enter- prise, and Web software systems and applications. He has extensive experience with the full stack of Web technologies, including both front-end and back-end design and implementation. Jose earned a B.S. in Chemistry from The College of William & Mary. Luke Hornof has a Ph.D. in Computer Science and has been part of several suc- cessful high-tech startups. His research in programming languages has resulted in more than a dozen peer-reviewed publications. He has also developed com- mercial software for the microprocessor, advertising, and music industries. His current interests include using analytics to improve web and mobile applications. Ben Peirce manages research and infrastructure at Spongecell, an advertising technology company. Prior to joining Spongecell, he worked in a variety of roles in healthcare technology startups, he and co-founded SET Media, an ad tech company focusing on video. He holds a PhD from Harvard University’s School of Engineering and Applied Sciences, where he studied control systems and robotics. v ffi rs.indd 11:51:43:AM 06/06/2014 Page v Acknowledgments Before writing a book, whenever I would see “there are too many people to thank” in the acknowledgements section it would seem cliché. It turns out that it is not so much cliché as a simple statement of fact. There really are more people to thank than could reasonably be put into print. If nothing else, including them all would make the book really hard to hold. However, there are a few people I would like to specifi cally thank for their contributions, knowing and unknowing, to the book. The fi rst, of course, is Robert Elliot at Wiley who seemed to think that a presentation that he had liked could possibly be a book of nearly 400 pages. Without him, this book simply wouldn’t exist. I would also like to thank Justin Langseth, who was not able to join me in writing this book but was my co-presenter at the talk that started the ball rolling. Hopefully, we will get a chance to reprise that experience. I would also like to thank my editors Charlotte, Rick, Jose, Luke, and Ben, led by Kelly Talbot, who helped fi nd and correct my many mistakes and kept the project on the straight and narrow. Any mistakes that may be left, you can be assured, are my fault. For less obvious contributions, I would like to thank all of the DDG regulars. At least half, probably more like 80%, of the software in this book is here as a direct result of a conversation I had with someone over a beer. Not too shabby for an informal, loose-knit gathering. Thanks to Mike for fi rst inviting me along and to Matt and Zack for hosting so many events over the years. Finally, I’d like to thank my colleagues over the years. You all deserve some sort of medal for putting up with my various harebrained schemes and tinker- ing. An especially big shout-out goes to the adBrite crew. We built a lot of cool stuff that I know for a fact is still cutting edge. Caroline Moon gets a big thank vii ffi rs.indd 11:51:43:AM 06/06/2014 Page vii viii Acknowledgments you for not being too concerned when her analytics folks wandered off and started playing with this newfangled “Hadoop” thing and started collecting more servers than she knew we had. I’d also especially like to thank Daniel Issen and Vadim Geshel. We didn’t always see eye-to-eye (and probably still don’t), but what you see in this book is here in large part to arguments I’ve had with those two. ffi rs.indd 11:51:43:AM 06/06/2014 Page viii Contents Introduction xv Chapter 1 Introduction to Streaming Data 1 Sources of Streaming Data 2 Operational Monitoring 3 Web Analytics 3 Online Advertising 4 Social Media 5 Mobile Data and the Internet of Things 5 Why Streaming Data Is Different 7 Always On, Always Flowing 7 Loosely Structured 8 High-Cardinality Storage 9 Infrastructures and Algorithms 10 Conclusion 10 Part I Streaming Analytics Architecture 13 Chapter 2 Designing Real-Time Streaming Architectures 15 Real-Time Architecture Components 16 Collection 16 Data Flow 17 Processing 19 Storage 20 Delivery 22 Features of a Real-Time Architecture 24 High Availability 24 Low Latency 25 Horizontal Scalability 26 ix ftoc.indd 05:30:33:PM 06/12/2014 Page ix x Contents Languages for Real-Time Programming 27 Java 27 Scala and Clojure 28 JavaScript 29 The Go Language 30 A Real-Time Architecture Checklist 30 Collection
Load more

Recommended publications
  • AUTOMATIC DESIGN of NONCRYPTOGRAPHIC HASH FUNCTIONS USING GENETIC PROGRAMMING, Computational Intelligence, 4, 798– 831
    Universidad uc3m Carlos Ill 0 -Archivo de Madrid This is a postprint version of the following published document: Estébanez, C., Saez, Y., Recio, G., and Isasi, P. (2014), AUTOMATIC DESIGN OF NONCRYPTOGRAPHIC HASH FUNCTIONS USING GENETIC PROGRAMMING, Computational Intelligence, 4, 798– 831 DOI: https://doi.org/10.1111/coin.12033 © 2014 Wiley Periodicals, Inc. AUTOMATIC DESIGN OF NONCRYPTOGRAPHIC HASH FUNCTIONS USING GENETIC PROGRAMMING CESAR ESTEBANEZ, YAGO SAEZ, GUSTAVO RECIO, AND PEDRO ISASI Department of Computer Science, Universidad Carlos III de Madrid, Madrid, Spain Noncryptographic hash functions have an immense number of important practical applications owing to their powerful search properties. However, those properties critically depend on good designs: Inappropriately chosen hash functions are a very common source of performance losses. On the other hand, hash functions are difficult to design: They are extremely nonlinear and counterintuitive, and relationships between the variables are often intricate and obscure. In this work, we demonstrate the utility of genetic programming (GP) and avalanche effect to automatically generate noncryptographic hashes that can compete with state-of-the-art hash functions. We describe the design and implementation of our system, called GP-hash, and its fitness function, based on avalanche properties. Also, we experimentally identify good terminal and function sets and parameters for this task, providing interesting information for future research in this topic. Using GP-hash, we were able to generate two different families of noncryptographic hashes. These hashes are able to compete with a selection of the most important functions of the hashing literature, most of them widely used in the industry and created by world-class hashing experts with years of experience.
    [Show full text]
  • Open Source Used in Quantum SON Suite 18C
    Open Source Used In Cisco SON Suite R18C Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-185964180 Open Source Used In Cisco SON Suite R18C 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-185964180 Contents 1.1 argparse 1.2.1 1.1.1 Available under license 1.2 blinker 1.3 1.2.1 Available under license 1.3 Boost 1.35.0 1.3.1 Available under license 1.4 Bunch 1.0.1 1.4.1 Available under license 1.5 colorama 0.2.4 1.5.1 Available under license 1.6 colorlog 0.6.0 1.6.1 Available under license 1.7 coverage 3.5.1 1.7.1 Available under license 1.8 cssmin 0.1.4 1.8.1 Available under license 1.9 cyrus-sasl 2.1.26 1.9.1 Available under license 1.10 cyrus-sasl/apsl subpart 2.1.26 1.10.1 Available under license 1.11 cyrus-sasl/cmu subpart 2.1.26 1.11.1 Notifications 1.11.2 Available under license 1.12 cyrus-sasl/eric young subpart 2.1.26 1.12.1 Notifications 1.12.2 Available under license Open Source Used In Cisco SON Suite R18C 2 1.13 distribute 0.6.34
    [Show full text]
  • Implementation of the Programming Language Dino – a Case Study in Dynamic Language Performance
    Implementation of the Programming Language Dino – A Case Study in Dynamic Language Performance Vladimir N. Makarov Red Hat [email protected] Abstract design of the language, its type system and particular features such The article gives a brief overview of the current state of program- as multithreading, heterogeneous extensible arrays, array slices, ming language Dino in order to see where its stands between other associative tables, first-class functions, pattern-matching, as well dynamic programming languages. Then it describes the current im- as Dino’s unique approach to class inheritance via the ‘use’ class plementation, used tools and major implementation decisions in- composition operator. cluding how to implement a stable, portable and simple JIT com- The second part of the article describes Dino’s implementation. piler. We outline the overall structure of the Dino interpreter and just- We study the effect of major implementation decisions on the in-time compiler (JIT) and the design of the byte code and major performance of Dino on x86-64, AARCH64, and Powerpc64. In optimizations. We also describe implementation details such as brief, the performance of some model benchmark on x86-64 was the garbage collection system, the algorithms underlying Dino’s improved by 3.1 times after moving from a stack based virtual data structures, Dino’s built-in profiling system, and the various machine to a register-transfer architecture, a further 1.5 times by tools and libraries used in the implementation. Our goal is to give adding byte code combining, a further 2.3 times through the use an overview of the major implementation decisions involved in of JIT, and a further 4.4 times by performing type inference with a dynamic language, including how to implement a stable and byte code specialization, with a resulting overall performance im- portable JIT.
    [Show full text]
  • Hash-Flooding Dos Reloaded
    Hash-flooding DoS reloaded: Hash flooding begins? attacks and defenses July 1998 article Jean-Philippe Aumasson, “Designing and attacking Kudelski Security (NAGRA) port scan detection tools” by Solar Designer (Alexander D. J. Bernstein, Peslyak) in Phrack Magazine: University of Illinois at Chicago & Technische Universiteit Eindhoven “In scanlogd, I’m using a hash table to lookup source addresses. Martin Boßlet, This works very well for the Ruby Core Team typical case ::: average lookup time is better than that of a binary search. ::: Hash-flooding DoS reloaded: Hash flooding begins? However, an attacker can attacks and defenses choose her addresses (most July 1998 article likely spoofed) to cause hash Jean-Philippe Aumasson, “Designing and attacking collisions, effectively replacing the Kudelski Security (NAGRA) port scan detection tools” hash table lookup with a linear by Solar Designer (Alexander D. J. Bernstein, search. Depending on how many Peslyak) in Phrack Magazine: University of Illinois at Chicago & entries we keep, this might make Technische Universiteit Eindhoven “In scanlogd, I’m using a hash scanlogd not be able to pick table to lookup source addresses. ::: Martin Boßlet, new packets up in time. I’ve This works very well for the Ruby Core Team solved this problem by limiting typical case ::: average lookup the number of hash collisions, and time is better than that of a discarding the oldest entry with binary search. ::: the same hash value when the limit is reached. Hash-flooding DoS reloaded: Hash flooding begins? However, an attacker can attacks and defenses choose her addresses (most July 1998 article likely spoofed) to cause hash Jean-Philippe Aumasson, “Designing and attacking collisions, effectively replacing the Kudelski Security (NAGRA) port scan detection tools” hash table lookup with a linear by Solar Designer (Alexander D.
    [Show full text]
  • Automated Malware Analysis Report for Phish Survey.Js
    ID: 382893 Sample Name: phish_survey.js Cookbook: default.jbs Time: 20:27:42 Date: 06/04/2021 Version: 31.0.0 Emerald Table of Contents Table of Contents 2 Analysis Report phish_survey.js 3 Overview 3 General Information 3 Detection 3 Signatures 3 Classification 3 Startup 3 Malware Configuration 3 Yara Overview 3 Sigma Overview 3 Signature Overview 3 Mitre Att&ck Matrix 4 Behavior Graph 4 Screenshots 5 Thumbnails 5 Antivirus, Machine Learning and Genetic Malware Detection 6 Initial Sample 6 Dropped Files 6 Unpacked PE Files 6 Domains 6 URLs 6 Domains and IPs 7 Contacted Domains 7 URLs from Memory and Binaries 7 Contacted IPs 8 General Information 8 Simulations 9 Behavior and APIs 9 Joe Sandbox View / Context 9 IPs 9 Domains 9 ASN 9 JA3 Fingerprints 9 Dropped Files 9 Created / dropped Files 9 Static File Info 9 General 9 File Icon 9 Network Behavior 10 Code Manipulations 10 Statistics 10 System Behavior 10 Analysis Process: wscript.exe PID: 6168 Parent PID: 3388 10 General 10 File Activities 10 Disassembly 10 Code Analysis 10 Copyright Joe Security LLC 2021 Page 2 of 10 Analysis Report phish_survey.js Overview General Information Detection Signatures Classification Sample phish_survey.js Name: FFoouunndd WSSHH tttiiimeerrr fffoorrr JJaavvaassccrrriiippttt oorrr VV… Analysis ID: 382893 JFJaaovvuaan d/// VVWBBSSSHccr rritiipipmttt feffiiillrlee f owwrii ittJthha vveaerrsryyc rllloiopnntg go srs …V MD5: b3c1f68ef7299a7… PJParrrovogagrr ra/a mVB ddSooceersisp ntn ofoitltte ss hwhooitwhw vmeuurycc hhlo aanccgttt iiivsviii… SHA1: b8e9103fffa864a…
    [Show full text]
  • Symantec Data Insight 4.5.1 Third-Party Attributions
    Symantec Data Insight Third-Party Attributions Guide 4.5.1 October 2014 Symantec Proprietary and Confidential Symantec Data Insight 4.5 Third-Party Attributions Guide 4.5.1 Documentation version: 4.5.1 Rev 0 Legal Notice Copyright © 2014 Symantec Corporation. All rights reserved. Symantec, the Symantec Logo, the Checkmark Logo are trademarks or registered trademarks of Symantec Corporation or its affiliates in the U.S. and other countries. Other names may be trademarks of their respective owners. This Symantec product may contain third party software for which Symantec is required to provide attribution to the third party (“Third Party Programs”). Some of the Third Party Programs are available under open source or free software licenses. The License Agreement accompanying the Software does not alter any rights or obligations you may have under those open source or free software licenses. Please see the Third Party Legal Notice Appendix to this Documentation or TPIP ReadMe File accompanying this Symantec product for more information on the Third Party Programs. The product described in this document is distributed under licenses restricting its use, copying, distribution, and decompilation/reverse engineering. No part of this document may be reproduced in any form by any means without prior written authorization of Symantec Corporation and its licensors, if any. THE DOCUMENTATION IS PROVIDED "AS IS" AND ALL EXPRESS OR IMPLIED CONDITIONS, REPRESENTATIONS AND WARRANTIES, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT, ARE DISCLAIMED, EXCEPT TO THE EXTENT THAT SUCH DISCLAIMERS ARE HELD TO BE LEGALLY INVALID. SYMANTEC CORPORATION SHALL NOT BE LIABLE FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH THE FURNISHING, PERFORMANCE, OR USE OF THIS DOCUMENTATION.
    [Show full text]
  • Fundamental Data Structures Contents
    Fundamental Data Structures Contents 1 Introduction 1 1.1 Abstract data type ........................................... 1 1.1.1 Examples ........................................... 1 1.1.2 Introduction .......................................... 2 1.1.3 Defining an abstract data type ................................. 2 1.1.4 Advantages of abstract data typing .............................. 4 1.1.5 Typical operations ...................................... 4 1.1.6 Examples ........................................... 5 1.1.7 Implementation ........................................ 5 1.1.8 See also ............................................ 6 1.1.9 Notes ............................................. 6 1.1.10 References .......................................... 6 1.1.11 Further ............................................ 7 1.1.12 External links ......................................... 7 1.2 Data structure ............................................. 7 1.2.1 Overview ........................................... 7 1.2.2 Examples ........................................... 7 1.2.3 Language support ....................................... 8 1.2.4 See also ............................................ 8 1.2.5 References .......................................... 8 1.2.6 Further reading ........................................ 8 1.2.7 External links ......................................... 9 1.3 Analysis of algorithms ......................................... 9 1.3.1 Cost models ......................................... 9 1.3.2 Run-time analysis
    [Show full text]
  • Antares: a Scalable, Efficient Platform for Stream, Historic
    School of Computing Science Antares: A Scalable, Efficient Platform for Stream, Historic, Combined and Geospatial Querying Rebecca Simmonds Submitted for the degree of Doctor of Philosophy in the School of Computing Science, Newcastle University June 2016 c 2016, Rebecca Simmonds Abstract Traditional methods for storing and analysing data are proving inadequate for process- ing \Big Data". This is due to its volume, and the rate at which it is being generated. The limitations of current technologies are further exacerbated by the increased de- mand for applications which allow users to access and interact with data as soon as it is generated. Near real-time analysis such as this can be partially supported by stream processing systems, however they currently lack the ability to store data for efficient historic processing: many applications require a combination of near real-time and historic data analysis. This thesis investigates this problem, and describes and evaluates a novel approach for addressing it. Antares is a layered framework that has been designed to exploit and extend the scalability of NoSQL databases to support low latency querying and high throughput rates for both stream and historic data analysis simultaneously. Antares began as a company funded project, sponsored by Red Hat the motivation was to identify a new technology which could provide scalable analysis of data, both stream and historic. The motivation for this was to explore new methods for supporting scale and efficiency, for example a layered approach. A layered approach would exploit the scale of historic stores and the speed of in-memory processing. New technologies were investigates to identify current mechanisms and suggest a means of improvement.
    [Show full text]
  • An Enhanced Non-Cryptographic Hash Function
    International Journal of Computer Applications (0975 – 8887) Volume 176 – No. 15, April 2020 An Enhanced Non-Cryptographic Hash Function Vivian Akoto-Adjepong Michael Asante Steve Okyere-Gyamfi University of Energy and Natural Kwame Nkrumah University of Christian Service University Resources Science and Technology College Department of Computer Science Department of Computer Science Department of Computer Science and Informatics Private Mail Bag, KNUST, Kumasi, and Information Technology P. O. Box 214 Sunyani, Ghana Ghana P. O. Box 3110 Kumasi, Ghana ABSTRACT In order to locate and retrieve information, hashing is a How to store information for it to be searched and retrieved recommended scheme because is effective and efficient [18]. efficiently is one of the fundamental problems in computer A suitable hash function and strategy must be used to solve science. There exists sequential search that support operation particular problems or for specific application. This will help such as INSERT, DELETE and RETRIVAL in O (n log (n)) efficient use of memory space and reduce access time. expected time in operations. Therefore in many applications where these operations are needed, hashing provides a way to There exist different types of hash algorithms such as non- reduce expected time to O (1).There are many different types cryptographic hash algorithms or functions, cryptographic of hashing algorithms or functions such as cryptographic hash hash algorithms or functions, checksums and cyclic functions, non-cryptographic hash function, checksums and redundancy checks [1][3]. cyclic redundancy checks. Non-cryptographic hash functions Independent of the inputs of a hash functions, they are (NCHFs) take a string as input and compute an integer output optimized to work very well in different scenarios.
    [Show full text]
  • The Power of Evil Choices in Bloom Filters Thomas Gerbet, Amrit Kumar, Cédric Lauradoux
    The Power of Evil Choices in Bloom Filters Thomas Gerbet, Amrit Kumar, Cédric Lauradoux To cite this version: Thomas Gerbet, Amrit Kumar, Cédric Lauradoux. The Power of Evil Choices in Bloom Filters. [Research Report] RR-8627, INRIA Grenoble. 2014. hal-01082158v2 HAL Id: hal-01082158 https://hal.inria.fr/hal-01082158v2 Submitted on 24 Feb 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The Power of Evil Choices in Bloom Filters Thomas Gerbet, Amrit Kumar, Cédric Lauradoux RESEARCH REPORT N° 8627 November 2014 Project-Team Privatics ISSN 0249-6399 ISRN INRIA/RR--8627--FR+ENG The Power of Evil Choices in Bloom Filters Thomas Gerbet, Amrit Kumar, C´edricLauradoux Project-Team Privatics Research Report n° 8627 | November 2014 | 24 pages Abstract: A Bloom filter is a probabilistic hash-based data structure extensively used in software products including online security applications. This paper raises the following important question: Are Bloom filters correctly designed in a security context ? The answer is no and the reasons are multiple: bad choices of parameters, lack of adversary models and misused hash functions. Indeed, developers truncate cryptographic digests without a second thought on the security implications.
    [Show full text]
  • Qlikview-Third-Party-License-Terms.Pdf
    Third Party Software Attributions, Copyrights, Licenses and Disclosure QlikView® May 2021 Certain open source or other third-party software components are integrated and/or redistributed with various releases of the QlikView software. Such third-party components include terms and conditions, such as attribution and liability disclaimers (collectively "Third Party Disclosures",) for which disclosure is required by their respective owners. This document sets forth such Third Party Disclosures for the specified version of the QlikView software and/or its associated Connectors, as of the date set forth above. This Third Party Disclosure is also available within the Documentation for QlikView and its associated Connectors, as well as on the Qlik web page located at www.qlik.com/license-terms. NEITHER QLIKTECH INTERNATIONAL AB NOR ANY OF ITS AFFILIATES (COLLECTIVELY, “QLIK”) MAKES ANY REPRESENTATION, WARRANTY OR OTHER COMMITMENT REGARDING SUCH THIRD PARTY COMPONENTS. AsyncLazy.cs • Copyright © 2014 Stephen Cleary • URL: https://github.com/StephenCleary/AsyncEx/tree/master/src/Nito.AsyncEx.Coordination • Version 5.0 • License: MIT AWS SDK • Copyright Amazon.com, Inc. or its affiliates. All rights reserved • URL: https://aws.amazon.com/sdk-for-go/ • Version: 1.34.28 • License: Apache-2.0 Aws-sdk-cpp • Copyright © 2010-2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. • URL: https://github.com/aws/aws-sdk-cpp • Version: 1.8.148.1 • License: Apache-2.0 BigCache • Copyright © 2004 Allegro Tech • URL: github.com/allegro/bigcache • Version: 1.2.1 • License: Apache-2.0 Boost C++ Libraries • Copyright © 1999-2015 Boost Contributors • URL: http://www.boost.org • Version: 1.71.0.2 • License: Boost License Bouncy Castle Cryptos API • Copyright © 2000-2017 The Legion of the Bouncy Castle Inc.
    [Show full text]
  • Licensing Information User Manual for Autonomous Database on Shared Exadata Infrastructure
    Oracle® Cloud Licensing Information User Manual for Autonomous Database on Shared Exadata Infrastructure F40733-02 April 2021 Oracle Cloud Licensing Information User Manual for Autonomous Database on Shared Exadata Infrastructure, F40733-02 Copyright © 2021, 2021, Oracle and/or its affiliates. Primary Author: Thomas Van Raalte This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer
    [Show full text]