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Java Linksammlung
JAVA LINKSAMMLUNG LerneProgrammieren.de - 2020 Java einfach lernen (klicke hier) JAVA LINKSAMMLUNG INHALTSVERZEICHNIS Build ........................................................................................................................................................... 4 Caching ....................................................................................................................................................... 4 CLI ............................................................................................................................................................... 4 Cluster-Verwaltung .................................................................................................................................... 5 Code-Analyse ............................................................................................................................................. 5 Code-Generators ........................................................................................................................................ 5 Compiler ..................................................................................................................................................... 6 Konfiguration ............................................................................................................................................. 6 CSV ............................................................................................................................................................. 6 Daten-Strukturen -
Learning Apache Mahout Classification Table of Contents
Learning Apache Mahout Classification Table of Contents Learning Apache Mahout Classification Credits About the Author About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Classification in Data Analysis Introducing the classification Application of the classification system Working of the classification system Classification algorithms Model evaluation techniques The confusion matrix The Receiver Operating Characteristics (ROC) graph Area under the ROC curve The entropy matrix Summary 2. Apache Mahout Introducing Apache Mahout Algorithms supported in Mahout Reasons for Mahout being a good choice for classification Installing Mahout Building Mahout from source using Maven Installing Maven Building Mahout code Setting up a development environment using Eclipse Setting up Mahout for a Windows user Summary 3. Learning Logistic Regression / SGD Using Mahout Introducing regression Understanding linear regression Cost function Gradient descent Logistic regression Stochastic Gradient Descent Using Mahout for logistic regression Summary 4. Learning the Naïve Bayes Classification Using Mahout Introducing conditional probability and the Bayes rule Understanding the Naïve Bayes algorithm Understanding the terms used in text classification Using the Naïve Bayes algorithm in Apache Mahout Summary 5. Learning the Hidden Markov Model Using Mahout Deterministic and nondeterministic patterns The Markov process Introducing the Hidden Markov Model Using Mahout for the Hidden Markov Model Summary 6. Learning Random Forest Using Mahout Decision tree Random forest Using Mahout for Random forest Steps to use the Random forest algorithm in Mahout Summary 7. -
FREE DOWNLOAD COMPONENTS/THIRD PARTY TERMS and CONDITIONS PROGRAM: Sybase® Replication Server® - Data Assurance Option Version 15.6.X (All Platforms)
FREE DOWNLOAD COMPONENTS/THIRD PARTY TERMS AND CONDITIONS PROGRAM: Sybase® Replication Server® - Data Assurance Option version 15.6.x (all platforms) The Program contains open source and other free download components as identified below. Third party license terms and other third party-required notices are provided below. Apache Software Foundation Components: Derby, Commons Digester and Commons Logging The Program includes software developed by the Apache Software Foundation. The Apache components are provided subject to the Apache License v2.0. Copyright© 1999-2010 The Apache Software Foundation. All rights reserved. A copy of the Apache License v2.0 is attached as Attachment 1. The Derby includes the third party components listed in Attachment 2. Sun Microsystems Component: Java Runtime Environment 1.6 The Java Runtime Environment includes the third party components listed in Attachment 3. Flexera Software Component: InstallAnywhere No third party license terms or additional notices required. Startertool.com Component: Startertool No third party license terms or additional notices required. Sun Microsystems Component: Java Servlets No third party license terms or additional notices required. Sybase Common Security Infrastructure Components This version of the Program includes Common Security Infrastructure v3.1 which contains the following additional open source and free download components: Apache Software Foundation Components: Apache xml-sec, Commons BeanUtils, Commons Digester, Commons Validator, Commons Logging, Commons FileUpload and Struts The Program includes software developed by the Apache Software Foundation. Copyright© 1999- 2004 The Apache Software Foundation. All rights reserved. A copy of the Apache License v2.0 is at Attachment 1. OpenSAML.org Component: OpenSAML The Program includes software made available by OpenSAML.org. -
Notice Report
NetApp Notice Report Copyright 2020 About this document The following copyright statements and licenses apply to the software components that are distributed with the Active IQ Platform product released on 2020-05-13 05:40:47. This product does not necessarily use all the software components referred to below. Where required, source code is published at the following location. ftp://ftp.netapp.com/frm-ntap/opensource 1 Components: Component License Achilles Core 5.3.0 Apache License 2.0 An open source Java toolkit for Amazon S3 0.9.0 Apache License 2.0 Apache Avro 1.7.6-cdh5.4.2.1.1 Apache License 2.0 Apache Avro 1.7.6-cdh5.4.4 Apache License 2.0 Apache Commons BeanUtils 1.7.0 Apache License 2.0 Apache Commons BeanUtils 1.8.0 Apache License 2.0 Apache Commons CLI 1.2 Apache License 2.0 Apache Commons Codec 1.9 Apache License 2.0 Apache Commons Collections 3.2.1 Apache License 2.0 Apache Commons Compress 1.4.1 Apache License 2.0 Apache Commons Configuration 1.6 Apache License 2.0 Apache Commons Digester 1.8 Apache License 1.1 Apache Commons Lang 2.6 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 Apache Commons Math 3.1.1 Apache License 2.0 Apache Commons Net 3.1 Apache License 2.0 Apache Directory API ASN.1 API 1.0.0-M20 Apache License 2.0 Apache Directory LDAP API Utilities 1.0.0-M19 Apache License 2.0 Apache Directory LDAP API Utilities 1.0.0-M20 Apache License 2.0 Apache Directory Studio 2.0.0-M15 Apache License 2.0 Apache Directory Studio 2.0.0-M20 Apache License 2.0 2 Apache Hadoop Annotations 2.6.0-cdh5.4.4 Apache -
Declarative Languages for Big Streaming Data a Database Perspective
Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data. -
Apache Mahout User Recommender
Apache Mahout User Recommender Whiniest Peirce upstage her russias so isochronously that Mead scat very sore. Indicative and wooden Bartholomeus reports her Renfrew whets wondrously or emulsifies correspondingly, is Bennie cranky? Sidnee overflies esuriently while effaceable Rodrigo diabolizes lamentably or rumpling conversationally. Mathematically analyzing how frequent user experience for you can provide these are using intelligent algorithms labeled with. My lantern is this. The prior data set is a search for your recommendations help recommendation. This architecture is prepared to alarm the needs of Netflix, in order say make their choices in your timely manner. In the thresholdbased selection, Support Vector Machines and thrift on. Early adopter architecture must also likely to users to make mahout apache mahout to. It up thus quick to access how valuable recommender systems, creating a partially combined system and grade set. Students that achieve good grades in all their years of study are likely to find work and proceed to have a successful career using the knowledge they have gained from their studies. You may change your ad preferences anytime. This user which users dataset contains methods and apache mahout is. Make Alpine wait until Livewire is finished rendering to often its thing. It can be mahout apache mahout core component can i have not buy a user increased which users who as a technique of courses within seconds. They interact thus far be able to exit an informed decision in duration to maximise both their enjoyment of their studies and their agenda of successful academic performance. Collaborative competitive filtering: Learning recommender using context of user choice. -
Is a SPARQL Endpoint a Good Way to Manage Nursing Documentation
Is a SPARQL Endpoint a Good way to Manage Nursing Documentation Is a SPARQL Endpoint a good way to Manage Nursing Documentation by Muhammad Aslam Supervisor: Associate Professor Jan Pettersen Nytun Project report for Master Thesis in Information & Communication Technology IKT 590 in Spring 2014 University of Agder Faculty of Engineering and Science Grimstad, 2 June 2014 Status: Final Keywords: Semantic Web, Ontology, Jena, SPARQL, Triple Store, Relational Database Page 1 of 86 Is a SPARQL Endpoint a Good way to Manage Nursing Documentation Abstract. In Semantic Web there are different technologies available, among these technologies ontologies are considered a basic technology to promote semantic management and activities. An ontology is capable to exhibits a common, shareable and reusable view of a specific application domain, and they give meaning to information structures that are exchanged by information systems [63]. In this project our main goal is to develop an application that helps to store and manage the patient related clinical data. For this reason first we made an ontology, in ontology we add some patient related records. After that we made a Java application in which we read this ontology by the help of Jena. Then we checked this application with some other database solutions such as Triple Store (Jena TDB) and Relational database (Jena SDB). After that we performed SPARQL Queries to get results that reads from databases we have used, on the basis of results that we received after performing SPARQL Queries, we made an analysis on the performance and efficiency of databases. In these results we found that Triple Stores (Jena TDB) have capabilities to response very fast among other databases. -
Jersey 1.8 User Guide Jersey 1.8 User Guide Table of Contents
Jersey 1.8 User Guide Jersey 1.8 User Guide Table of Contents Preface ............................................................................................................................ ix 1. Getting Started ............................................................................................................... 1 1.1. Creating a root resource ........................................................................................ 2 1.2. Deploying the root resource ................................................................................... 3 1.3. Testing the root resource ....................................................................................... 3 1.4. Here's one Paul created earlier ................................................................................ 4 2. Overview of JAX-RS 1.1 ................................................................................................. 5 2.1. Root Resource Classes .......................................................................................... 5 2.1.1. @Path ...................................................................................................... 5 2.1.2. HTTP Methods .......................................................................................... 6 2.1.3. @Produces ................................................................................................ 7 2.1.4. @Consumes .............................................................................................. 9 2.2. Deploying a RESTful Web Service ......................................................................... -
Domain Landscape Review and Data Value Chain Definition
Ref. Ares(2018)2305100 - 30/04/2018 H2020 - INDUSTRIAL LEADERSHIP - Information and Communication Technologies (ICT) ICT-14-2016-2017: Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation ICARUS: “Aviation-driven Data Value Chain for Diversified Global and Local Operations” D1.1 – Domain Landscape Review and Data Value Chain Definition Workpackage: WP1 – ICARUS Data Value Chain Elaboration Authors: University of Cyprus (UCY), Suite5, UBITECH, CINECA, AIA, PACE, ISI, CELLOCK, TXT, OAG Status: Final Classification: Public Date: 30/04/2018 Version: 1.00 Disclaimer: The ICARUS project is co-funded by the Horizon 2020 Programme of the European Union. The information and views set out in this publication are those of the author(s) and do not necessarily reflect the official opinion of the European Communities. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. © Copyright in this document remains vested with the ICARUS Partners. D1.1 - Domain Landscape Review and Data Value Chain Definition ICARUS Project Profile Grant Agreement No.: 780792 Acronym: ICARUS Aviation-driven Data Value Chain for Diversified Global and Local Title: Operations URL: http://www.icarus2020.aero Start Date: 01/01/2018 Duration: 36 months Partners UBITECH (UBITECH) Greece ENGINEERING - INGEGNERIA INFORMATICA SPA (ENG) Italy PACE Aerospace Engineering and Information Technology GmbH (PACE) Germany SUITE5 DATA -
Qualifikationsprofil #10309
QUALIFIKATIONSPROFIL #10309 ALLGEMEINE DATEN Geburtsjahr: 1972 Ausbildung: Abitur Diplom, Informatik, (TU, Kaiserslautern) Fremdsprachen: Englisch, Französisch Spezialgebiete: Kubernetes KENNTNISSE Tools Active Directory Apache Application Case CATIA CVS Eclipse Exchange Framework GUI Innovator ITIL J2EE JMS LDAP Lotus Notes make MS Exchange MS Outlook MS-Exchange MS-Office MS-Visual Studio NetBeans OSGI RACF SAS sendmail Together Turbine UML VMWare .NET ADS ANT ASP ASP.NET Flash GEnie IDES Image Intellij IDEA IPC Jackson JBOSS Lex MS-Visio ODBC Oracle Application Server OWL PGP SPSS SQS TesserAct Tivoli Toolbook Total Transform Visio Weblogic WebSphere YACC Tätigkeiten Administration Analyse Beratung Design Dokumentation KI Konzeption Optimierung Support Vertrieb Sprachen Ajax Basic C C# C++ Cobol Delphi ETL Fortran Java JavaScript Natural Perl PHP PL/I PL-SQL Python SAL Smalltalk SQL ABAP Atlas Clips Delta FOCUS HTML Nomad Pascal SPL Spring TAL XML Detaillierte Komponenten AM BI FS-BA MDM PDM PM BW CO FI LO PP Datenbanken Approach IBM Microsoft Object Store Oracle Progress Sybase DMS ISAM JDBC mySQL DC/Netzwerke ATM DDS Gateway HBCI Hub Internet Intranet OpenSSL SSL VPN Asynchronous CISCO Router DNS DSL Firewall Gateways HTTP RFC Router Samba Sockets Switches Finance Business Intelligence Excel Konsolidierung Management Projektleiter Reporting Testing Wertpapiere Einkauf CAD Systeme CATIA V5 sonstige Hardware Digital HP PC Scanner Siemens Spark Teradata Bus FileNet NeXT SUN Switching Tools, Methoden Docker Go Kubernetes Rational RUP -
Tracking Known Security Vulnerabilities in Third-Party Components
Tracking known security vulnerabilities in third-party components Master’s Thesis Mircea Cadariu Tracking known security vulnerabilities in third-party components THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER SCIENCE by Mircea Cadariu born in Brasov, Romania Software Engineering Research Group Software Improvement Group Department of Software Technology Rembrandt Tower, 15th floor Faculty EEMCS, Delft University of Technology Amstelplein 1 - 1096HA Delft, the Netherlands Amsterdam, the Netherlands www.ewi.tudelft.nl www.sig.eu c 2014 Mircea Cadariu. All rights reserved. Tracking known security vulnerabilities in third-party components Author: Mircea Cadariu Student id: 4252373 Email: [email protected] Abstract Known security vulnerabilities are introduced in software systems as a result of de- pending on third-party components. These documented software weaknesses are hiding in plain sight and represent the lowest hanging fruit for attackers. Despite the risk they introduce for software systems, it has been shown that developers consistently download vulnerable components from public repositories. We show that these downloads indeed find their way in many industrial and open-source software systems. In order to improve the status quo, we introduce the Vulnerability Alert Service, a tool-based process to track known vulnerabilities in software projects throughout the development process. Its usefulness has been empirically validated in the context of the external software product quality monitoring service offered by the Software Improvement Group, a software consultancy company based in Amsterdam, the Netherlands. Thesis Committee: Chair: Prof. Dr. A. van Deursen, Faculty EEMCS, TU Delft University supervisor: Prof. Dr. A. -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol