Trifacta Data Preparation for Amazon Redshift and S3 Must Be Deployed Into an Existing Virtual Private Cloud (VPC)
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Poly Videoos Offer of Source for Open Source Software 3.6.0
OFFER OF SOURCE FOR 3.6.0 | 2021 | 3725-85857-010A OPEN SOURCE SOFTWARE August Poly VideoOS Software Contents Offer of Source for Open Source Software .............................................................................. 1 Open Source Software ............................................................................................................. 2 Qualcomm Platform Licenses ............................................................................................................. 2 List of Open Source Software .................................................................................................. 2 Poly G7500, Poly Studio X50, and Poly Studio X30 .......................................................................... 2 Poly Microphone IP Adapter ............................................................................................................. 13 Poly IP Table Microphone and Poly IP Ceiling Microphone ............................................................. 18 Poly TC8 and Poly Control Application ............................................................................................. 21 Get Help ..................................................................................................................................... 22 Related Poly and Partner Resources ..................................................................................... 22 Privacy Policy ........................................................................................................................... -
A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem
Noname manuscript No. (will be inserted by the editor) A Comprehensive Study of Bloated Dependencies in the Maven Ecosystem César Soto-Valero · Nicolas Harrand · Martin Monperrus · Benoit Baudry Received: date / Accepted: date Abstract Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application’s code and its ex- ternal dependencies, and automate several software development tasks. How- ever, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that the build tool packages with the application’s compiled code but that are actually not necessary to build and run the application. This phenomenon artificially grows the size of the built binary and increases maintenance effort. We propose a tool, called DepClean, to analyze the presence of bloated dependencies in Maven artifacts. We ana- lyze 9; 639 Java artifacts hosted on Maven Central, which include a total of 723; 444 dependency relationships. Our key result is that 75:1% of the analyzed dependency relationships are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts up to 1=4 of its current count. We also perform a qualitative study with 30 notable open-source projects. Our results indicate that developers pay attention to their dependencies and are willing to remove bloated dependencies: 18/21 answered pull requests were accepted and merged by developers, removing 131 dependencies in total. -
Gateway Licensing Information User Manual Version 19
Gateway Licensing Information User Manual Version 19 December 2019 Contents Introduction ...................................................................................................................................... 5 Licensed Products, Restricted Use Licenses, and Prerequisite Products ........................................ 5 Primavera Gateway ................................................................................................................................ 5 Third Party Notices and/or Licenses ................................................................................................ 6 Bootstrap ................................................................................................................................................ 6 Commons Codec .................................................................................................................................... 6 Commons Compress .............................................................................................................................. 6 Commons IO ........................................................................................................................................... 7 Commons Net ......................................................................................................................................... 7 commons-vfs .......................................................................................................................................... 7 HttpComponents HttpClient .................................................................................................................. -
Evil Pickles: Dos Attacks Based on Object-Graph Engineering∗
Evil Pickles: DoS Attacks Based on Object-Graph Engineering∗ Jens Dietrich1, Kamil Jezek2, Shawn Rasheed3, Amjed Tahir4, and Alex Potanin5 1 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 2 NTIS – New Technologies for the Information Society Faculty of Applied Sciences, University of West Bohemia Pilsen, Czech Republic [email protected] 3 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 4 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 5 School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand [email protected] Abstract In recent years, multiple vulnerabilities exploiting the serialisation APIs of various programming languages, including Java, have been discovered. These vulnerabilities can be used to devise in- jection attacks, exploiting the presence of dynamic programming language features like reflection or dynamic proxies. In this paper, we investigate a new type of serialisation-related vulnerabilit- ies for Java that exploit the topology of object graphs constructed from classes of the standard library in a way that deserialisation leads to resource exhaustion, facilitating denial of service attacks. We analyse three such vulnerabilities that can be exploited to exhaust stack memory, heap memory and CPU time. We discuss the language and library design features that enable these vulnerabilities, and investigate whether these vulnerabilities can be ported to C#, Java- Script and Ruby. We present two case studies that demonstrate how the vulnerabilities can be used in attacks on two widely used servers, Jenkins deployed on Tomcat and JBoss. -
Executable Trigger-Action Comments
Executable Trigger-Action Comments Pengyu Nie, Rishabh Rai, Junyi Jessy Li, Sarfraz Khurshid, Raymond J. Mooney, and Milos Gligoric The University of Texas at Austin {pynie@,rrai.squared@,jessy@austin.,khurshid@ece.,mooney@cs.,gligoric@}utexas.edu ABSTRACT Wave project: “Remove this when HtmlViewImpl implements getAt- Natural language elements, e.g., todo comments, are frequently tributes”, etc.). We consider those comments where the trigger is used to communicate among the developers and to describe tasks expressed based on the state of the code repositories and actions that need to be performed (actions) when specific conditions hold require modifications of source or binary code. We call these com- in the code repository (triggers). As projects evolve, development ments trigger-action comments. processes change, and development teams reorganize, these com- Although trigger-action comments are ubiquitous, they are, like ments, because of their informal nature, frequently become irrele- other types of comments, written in natural language. Thus, as vant or forgotten. projects evolve, development processes change, and development We present the first technique, dubbed TrigIt, to specify trigger- teams reorganize, these comments frequently become irrelevant or action todo comments as executable statements. Thus, actions are forgotten. As an example, consider the following comment from executed automatically when triggers evaluate to true. TrigIt spec- the Apache Gobblin project [13]: “Remove once we commit any ifications are written in the host language (e.g., Java) and are evalu- other classes”. This comment, followed by an empty class, was in- ated as part of the build process. The triggers are specified as query cluded in revision 38ce024 (Dec 10, 2015) in package-info.java file statements over abstract syntax trees and abstract representation to force the javadoc tool to generate documentation for an empty of build configuration scripts, and the actions are specified as code package. -
Regeldokument
Master’s degree project Source code quality in connection to self-admitted technical debt Author: Alina Hrynko Supervisor: Morgan Ericsson Semester: VT20 Subject: Computer Science Abstract The importance of software code quality is increasing rapidly. With more code being written every day, its maintenance and support are becoming harder and more expensive. New automatic code review tools are developed to reach quality goals. One of these tools is SonarQube. However, people keep their leading role in the development process. Sometimes they sacrifice quality in order to speed up the development. This is called Technical Debt. In some particular cases, this process can be admitted by the developer. This is called Self-Admitted Technical Debt (SATD). Code quality can also be measured by such static code analysis tools as SonarQube. On this occasion, different issues can be detected. The purpose of this study is to find a connection between code quality issues, found by SonarQube and those marked as SATD. The research questions include: 1) Is there a connection between the size of the project and the SATD percentage? 2) Which types of issues are the most widespread in the code, marked by SATD? 3) Did the introduction of SATD influence the bug fixing time? As a result of research, a certain percentage of SATD was found. It is between 0%–20.83%. No connection between the size of the project and the percentage of SATD was found. There are certain issues that seem to relate to the SATD, such as “Duplicated code”, “Unused method parameters should be removed”, “Cognitive Complexity of methods should not be too high”, etc. -
Trifacta Data Preparation for Amazon Redshift and S3 Must Be Deployed Into an Existing Virtual Private Cloud (VPC)
Install Guide for Data Preparation for Amazon Redshift and S3 Version: 7.1 Doc Build Date: 05/26/2020 Copyright © Trifacta Inc. 2020 - All Rights Reserved. CONFIDENTIAL These materials (the “Documentation”) are the confidential and proprietary information of Trifacta Inc. and may not be reproduced, modified, or distributed without the prior written permission of Trifacta Inc. EXCEPT AS OTHERWISE PROVIDED IN AN EXPRESS WRITTEN AGREEMENT, TRIFACTA INC. PROVIDES THIS DOCUMENTATION AS-IS AND WITHOUT WARRANTY AND TRIFACTA INC. DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES TO THE EXTENT PERMITTED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE AND UNDER NO CIRCUMSTANCES WILL TRIFACTA INC. BE LIABLE FOR ANY AMOUNT GREATER THAN ONE HUNDRED DOLLARS ($100) BASED ON ANY USE OF THE DOCUMENTATION. For third-party license information, please select About Trifacta from the Help menu. 1. Quick Start . 4 1.1 Install from AWS Marketplace . 4 1.2 Upgrade for AWS Marketplace . 7 2. Configure . 8 2.1 Configure for AWS . 8 2.1.1 Configure for EC2 Role-Based Authentication . 14 2.1.2 Enable S3 Access . 16 2.1.2.1 Create Redshift Connections 28 3. Contact Support . 30 4. Legal 31 4.1 Third-Party License Information . 31 Page #3 Quick Start Install from AWS Marketplace Contents: Product Limitations Internet access Install Desktop Requirements Pre-requisites Install Steps - CloudFormation template SSH Access Troubleshooting SELinux Upgrade Documentation Related Topics This guide steps through the requirements and process for installing Trifacta® Data Preparation for Amazon Redshift and S3 through the AWS Marketplace. -
Portable Stateful Big Data Processing in Apache Beam
Portable stateful big data processing in Apache Beam Kenneth Knowles Apache Beam PMC Software Engineer @ Google https://s.apache.org/ffsf-2017-beam-state [email protected] / @KennKnowles Flink Forward San Francisco 2017 Agenda 1. What is Apache Beam? 2. State 3. Timers 4. Example & Little Demo What is Apache Beam? TL;DR (Flink draws it more like this) 4 DAGs, DAGs, DAGs Apache Beam Apache Flink Apache Cloud Hadoop Apache Apache Dataflow Spark Samza MapReduce Apache Apache Apache (paper) Storm Gearpump Apex (incubating) FlumeJava (paper) Heron MillWheel (paper) Dataflow Model (paper) 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Java fully managed input.apply( Apache Spark Sum.integersPerKey()) local, on-prem, cloud Sum Per Key Apache Apex Python local, on-prem, cloud input | Sum.PerKey() Apache Gearpump (incubating) ⋮ ⋮ 6 Apache Flink local, on-prem, The Beam Vision cloud Cloud Dataflow: Python fully managed input | KakaIO.read() Apache Spark local, on-prem, cloud KafkaIO Apache Apex ⋮ local, on-prem, cloud Apache Java Gearpump (incubating) class KafkaIO extends UnboundedSource { … } ⋮ 7 The Beam Model PTransform Pipeline PCollection (bounded or unbounded) 8 The Beam Model What are you computing? (read, map, reduce) Where in event time? (event time windowing) When in processing time are results produced? (triggers) How do refinements relate? (accumulation mode) 9 What are you computing? Read ParDo Grouping Composite Parallel connectors to Per element Group -
The Forrester Wave™: Streaming Analytics, Q3 2019 the 11 Providers That Matter Most and How They Stack up by Mike Gualtieri September 23, 2019
LICENSED FOR INDIVIDUAL USE ONLY The Forrester Wave™: Streaming Analytics, Q3 2019 The 11 Providers That Matter Most And How They Stack Up by Mike Gualtieri September 23, 2019 Why Read This Report Key Takeaways In our 26-criterion evaluation of streaming Software AG, IBM, Microsoft, Google, And analytics providers, we identified the 11 most TIBCO Software Lead The Pack significant ones — Alibaba, Amazon Web Forrester’s research uncovered a market in which Services, Cloudera, EsperTech, Google, IBM, Software AG, IBM, Microsoft, Google, and TIBCO Impetus, Microsoft, SAS, Software AG, and Software are Leaders; Cloudera, SAS, Amazon TIBCO Software — and researched, analyzed, Web Services, and Impetus are Strong Performers; and scored them. This report shows how each and EsperTech and Alibaba are Contenders. provider measures up and helps application Analytics Prowess, Scalability, And development and delivery (AD&D) professionals Deployment Freedom Are Key Differentiators select the right one for their needs. Depth and breadth of analytics types on streaming data are critical. But that is all for naught if streaming analytics vendors cannot also scale to handle potentially huge volumes of streaming data. Also, it’s critical that streaming analytics can be deployed where it is most needed, such as on-premises, in the cloud, and/ or at the edge. This PDF is only licensed for individual use when downloaded from forrester.com or reprints.forrester.com. All other distribution prohibited. FORRESTER.COM FOR APPLICATION DEVELOPMENT & DELIVERY PROFESSIONALS The Forrester Wave™: Streaming Analytics, Q3 2019 The 11 Providers That Matter Most And How They Stack Up by Mike Gualtieri with Srividya Sridharan and Robert Perdoni September 23, 2019 Table Of Contents Related Research Documents 2 Enterprises Must Take A Streaming-First The Future Of Machine Learning Is Unstoppable Approach To Analytics Predictions 2019: Artificial Intelligence 3 Evaluation Summary Predictions 2019: Business Insights 6 Vendor Offerings 6 Vendor Profiles Leaders Share reports with colleagues. -
Intrinsic Redundancy for Reliability and Beyond
Intrinsic Redundancy for Reliability and Beyond Alberto Goffi, Alessandra Gorla, Andrea Mattavelli, and Mauro Pezze` Abstract Software redundancy is an essential mechanism in engineering. Different forms of redundant design are the core technology of well-established reliability and fault tolerant mechanisms in traditional engineering as well as in software engineering. In this paper we discuss intrinsic software redundancy, a type of redundancy that is not added explicitly at design time to improve runtime reliability, but is natively present in modern software system due to independent design and development decisions. We introduce the concept of intrinsic redundancy, discuss its diffusion and the reasons for its presence in modern software systems, indicate how it can be automatically identified, and present some current and future applications of such form of redundancy to produce more reliable software systems at affordable costs. 1 Introduction Reliability, which is the ability of a system or component to perform its required functions under stated conditions for a specified period of time [1], is a key property of engineered products and in particular of software artifacts. Safety critical applications must meet the high reliability standards required for their field deployment, time- and business-critical applications must obey strong reliability requirements, everyday Alberto Goffi USI Universita` della Svizzera italiana, Switzerland, e-mail: alberto.goffi@usi.ch Alessandra Gorla IMDEA Software Institute, Spain, e-mail: [email protected] Andrea Mattavelli Imperial College London, United Kingdom, e-mail: [email protected] Mauro Pezze` USI Universita` della Svizzera italiana, Switzerland and University of Milano-Bicocca, Italy, e-mail: [email protected] 1 2 Alberto Goffi, Alessandra Gorla, Andrea Mattavelli, and Mauro Pezze` and commodity products shall meet less stringent, but still demanding customer requirements. -
Hitachi Ops Center V.10.2.0
Hitachi Ops Center V. 10.2.0 Open Source Software Packages Contact Information: Hitachi Ops Center Project Manager Hitachi Vantara LLC 2535 Augustine Drive Santa Clara, California 95054 Name of Product/Product Version License Component aesh 2.4 Apache License, Version 2.0 aesh Extensions 1.8 Apache License, Version 2.0 aesh Readline 2.0 Apache License, Version 2.0 aesh Terminal API 2.0 Apache License, Version 2.0 "Java Concurrency in Practice" 1.0-redhat- Creative Commons Attribution 2.5 Generic book annotations 4 @angular-builders/custom- 8.0.0-RC.0 The MIT License webpack @angular-devkit/build-angular 0.800.0-rc.2 The MIT License @angular-devkit/build-angular 0.803.25 The MIT License @angular-devkit/core 7.3.8 The MIT License @angular-devkit/schematics 7.3.8 The MIT License @angular/animations 7.2.15 The MIT License @angular/animations 8.2.14 The MIT License Name of Product/Product Version License Component @angular/cdk 7.3.7 The MIT License @angular/cli 8.0.0 The MIT License @angular/cli 8.3.25 The MIT License @angular/common 7.2.15 The MIT License @angular/common 8.2.14 The MIT License @angular/compiler 7.2.15 The MIT License @angular/compiler 8.2.14 The MIT License @angular/compiler-cli 8.2.14 The MIT License @angular/core 7.2.15 The MIT License @angular/forms 7.2.13 The MIT License @angular/forms 7.2.15 The MIT License @angular/forms 8.2.14 The MIT License @angular/forms 8.2.7 The MIT License @angular/language-service 8.2.14 The MIT License @angular/platform-browser 7.2.15 The MIT License Name of Product/Product Version License -
Scalable and Flexible Middleware for Dynamic Data Flows
SCALABLEANDFLEXIBLEMIDDLEWAREFORDYNAMIC DATAFLOWS stephan boomker Master thesis Computing Science Software Engineering and Distributed Systems Primaray RuG supervisor : Prof. Dr. M. Aiello Secondary RuG supervisor : Prof. Dr. P. Avgeriou Primary TNO supervisor : MSc. E. Harmsma Secondary TNO supervisor : MSc. E. Lazovik August 16, 2016 – version 1.0 [ August 16, 2016 at 11:47 – classicthesis version 1.0 ] Stephan Boomker: Scalable and flexible middleware for dynamic data flows, Master thesis Computing Science, © August 16, 2016 [ August 16, 2016 at 11:47 – classicthesis version 1.0 ] ABSTRACT Due to the concepts of Internet of Things and Big data, the traditional client-server architecture is not sufficient any more. One of the main reasons is wide range of expanding heterogeneous applications, data sources and environments. New forms of data processing require new architectures and techniques in order to be scalable, flexible and able to handle fast dynamic data flows. The backbone of all those objects, applications and users is called the middleware. This research goes about designing and implementing a middle- ware by taking into account different state of the art tools and tech- niques. To come up to a solution which is able to handle a flexible set of sources and models across organizational borders. At the same time it is de-centralized, distributed and, although de-central able to perform semantic based system integration centrally. This is accom- plished by introducing of an architecture containing a combination of data integration patterns, semantic storage and stream processing patterns. A reference implementation is presented of the proposed architec- ture based on Apache Camel framework. This prototype provides the ability to dynamically create and change flexible and distributed data flows during runtime.