CHAPTER ONE INTRODUCTION 1.1 Background of the Study Advances
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Return of Organization Exempt from Income
OMB No. 1545-0047 Return of Organization Exempt From Income Tax Form 990 Under section 501(c), 527, or 4947(a)(1) of the Internal Revenue Code (except black lung benefit trust or private foundation) Open to Public Department of the Treasury Internal Revenue Service The organization may have to use a copy of this return to satisfy state reporting requirements. Inspection A For the 2011 calendar year, or tax year beginning 5/1/2011 , and ending 4/30/2012 B Check if applicable: C Name of organization The Apache Software Foundation D Employer identification number Address change Doing Business As 47-0825376 Name change Number and street (or P.O. box if mail is not delivered to street address) Room/suite E Telephone number Initial return 1901 Munsey Drive (909) 374-9776 Terminated City or town, state or country, and ZIP + 4 Amended return Forest Hill MD 21050-2747 G Gross receipts $ 554,439 Application pending F Name and address of principal officer: H(a) Is this a group return for affiliates? Yes X No Jim Jagielski 1901 Munsey Drive, Forest Hill, MD 21050-2747 H(b) Are all affiliates included? Yes No I Tax-exempt status: X 501(c)(3) 501(c) ( ) (insert no.) 4947(a)(1) or 527 If "No," attach a list. (see instructions) J Website: http://www.apache.org/ H(c) Group exemption number K Form of organization: X Corporation Trust Association Other L Year of formation: 1999 M State of legal domicile: MD Part I Summary 1 Briefly describe the organization's mission or most significant activities: to provide open source software to the public that we sponsor free of charge 2 Check this box if the organization discontinued its operations or disposed of more than 25% of its net assets. -
Projects – Other Than Hadoop! Created By:-Samarjit Mahapatra [email protected]
Projects – other than Hadoop! Created By:-Samarjit Mahapatra [email protected] Mostly compatible with Hadoop/HDFS Apache Drill - provides low latency ad-hoc queries to many different data sources, including nested data. Inspired by Google's Dremel, Drill is designed to scale to 10,000 servers and query petabytes of data in seconds. Apache Hama - is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS for massive scientific computations such as matrix, graph and network algorithms. Akka - a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. ML-Hadoop - Hadoop implementation of Machine learning algorithms Shark - is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones. Apache Crunch - Java library provides a framework for writing, testing, and running MapReduce pipelines. Its goal is to make pipelines that are composed of many user-defined functions simple to write, easy to test, and efficient to run Azkaban - batch workflow job scheduler created at LinkedIn to run their Hadoop Jobs Apache Mesos - is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, MPI, Hypertable, Spark, and other applications on a dynamically shared pool of nodes. -
An Unobtrusive, Scalable Approach to Large Scale Software License Analysis
DRAT: An Unobtrusive, Scalable Approach to Large Scale Software License Analysis Chris A. Mattmann1,2, Ji-Hyun Oh1,2, Tyler Palsulich1*, Lewis John McGibbney1, Yolanda Gil2,3, Varun Ratnakar3 1Jet Propulsion Laboratory 2Computer Science Department 3USC Information Sciences Institute California Institute of Technology University of Southern California University of Southern California Pasadena, CA 91109 USA Los Angeles, CA 90089 USA Marina Del Rey, CA [email protected] {mattmann,jihyuno}@usc.edu {gil,varunr}@ isi.edu Abstract— The Apache Release Audit Tool (RAT) performs (OSI) for complying with open source software open source license auditing and checking, however definition, however, there exist slight differences among these RAT fails to successfully audit today's large code bases. Being a licenses [2]. For instance, GPL is a “copyleft” license that natural language processing (NLP) tool and a crawler, RAT only allows derivative works under the original license, marches through a code base, but uses rudimentary black lists whereas MIT license is a “permissive” license that grants the and white lists to navigate source code repositories, and often does a poor job of identifying source code versus binary files. In right to sublicense the code under any kind of license [2]. This addition RAT produces no incremental output and thus on code difference could affect architectural design of the software. bases that themselves are "Big Data", RAT could run for e.g., a Furthermore, circumstances are more complicated when month and still not provide any status report. We introduce people publish software under the multiple licenses. Distributed "RAT" or the Distributed Release Audit Tool Therefore, an automated tool for verifying software licenses in (DRAT). -
Avaliando a Dívida Técnica Em Produtos De Código Aberto Por Meio De Estudos Experimentais
UNIVERSIDADE FEDERAL DE GOIÁS INSTITUTO DE INFORMÁTICA IGOR RODRIGUES VIEIRA Avaliando a dívida técnica em produtos de código aberto por meio de estudos experimentais Goiânia 2014 IGOR RODRIGUES VIEIRA Avaliando a dívida técnica em produtos de código aberto por meio de estudos experimentais Dissertação apresentada ao Programa de Pós–Graduação do Instituto de Informática da Universidade Federal de Goiás, como requisito parcial para obtenção do título de Mestre em Ciência da Computação. Área de concentração: Ciência da Computação. Orientador: Prof. Dr. Auri Marcelo Rizzo Vincenzi Goiânia 2014 Ficha catalográfica elaborada automaticamente com os dados fornecidos pelo(a) autor(a), sob orientação do Sibi/UFG. Vieira, Igor Rodrigues Avaliando a dívida técnica em produtos de código aberto por meio de estudos experimentais [manuscrito] / Igor Rodrigues Vieira. - 2014. 100 f.: il. Orientador: Prof. Dr. Auri Marcelo Rizzo Vincenzi. Dissertação (Mestrado) - Universidade Federal de Goiás, Instituto de Informática (INF) , Programa de Pós-Graduação em Ciência da Computação, Goiânia, 2014. Bibliografia. Apêndice. Inclui algoritmos, lista de figuras, lista de tabelas. 1. Dívida técnica. 2. Qualidade de software. 3. Análise estática. 4. Produto de código aberto. 5. Estudo experimental. I. Vincenzi, Auri Marcelo Rizzo, orient. II. Título. Todos os direitos reservados. É proibida a reprodução total ou parcial do trabalho sem autorização da universidade, do autor e do orientador(a). Igor Rodrigues Vieira Graduado em Sistemas de Informação, pela Universidade Estadual de Goiás – UEG, com pós-graduação lato sensu em Desenvolvimento de Aplicações Web com Interfaces Ricas, pela Universidade Federal de Goiás – UFG. Foi Coordenador da Ouvidoria da UFG e, atualmente, é Analista de Tecnologia da Informação do Centro de Recursos Computacionais – CERCOMP/UFG. -
Mahasen: Distributed Storage Resource Broker K
Mahasen: Distributed Storage Resource Broker K. Perera, T. Kishanthan, H. Perera, D. Madola, Malaka Walpola, Srinath Perera To cite this version: K. Perera, T. Kishanthan, H. Perera, D. Madola, Malaka Walpola, et al.. Mahasen: Distributed Storage Resource Broker. 10th International Conference on Network and Parallel Computing (NPC), Sep 2013, Guiyang, China. pp.380-392, 10.1007/978-3-642-40820-5_32. hal-01513774 HAL Id: hal-01513774 https://hal.inria.fr/hal-01513774 Submitted on 25 Apr 2017 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. Distributed under a Creative Commons Attribution| 4.0 International License Mahasen: Distributed Storage Resource Broker K.D.A.K.S.Perera1, T Kishanthan1, H.A.S.Perera1, D.T.H.V.Madola1, Malaka Walpola1, Srinath Perera2 1 Computer Science and Engineering Department, University Of Moratuwa, Sri Lanka. {shelanrc, kshanth2101, ashansa.perera, hirunimadola, malaka.uom}@gmail.com 2 WSO2 Lanka, No 59, Flower Road, Colombo 07, Sri Lanka [email protected] Abstract. Modern day systems are facing an avalanche of data, and they are being forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weather Sensors), transaction logs, Web, social networks etc. -
LNCS 8147, Pp
Mahasen: Distributed Storage Resource Broker K.D.A.K.S. Perera1, T. Kishanthan1, H.A.S. Perera1, D.T.H.V. Madola1, Malaka Walpola1, and Srinath Perera2 1 Computer Science and Engineering Department, University Of Moratuwa, Sri Lanka {shelanrc,kshanth2101,ashansa.perera,hirunimadola, malaka.uom}@gmail.com 2 WSO2 Lanka, No. 59, Flower Road, Colombo 07, Sri Lanka [email protected] Abstract. Modern day systems are facing an avalanche of data, and they are be- ing forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weath- er Sensors), transaction logs, Web, social networks etc. As an example, weather sensors across the world generate a large amount of data throughout the year. Handling these and similar data require scalable, efficient, reliable and very large storages with support for efficient metadata based searching. This paper present Mahasen, a highly scalable storage for high volume data intensive ap- plications built on top of a peer-to-peer layer. In addition to scalable storage, Mahasen also supports efficient searching, built on top of the Distributed Hash table (DHT) 1 Introduction Currently United States collects weather data from many sources like Doppler readers deployed across the country, aircrafts, mobile towers and Balloons etc. These sensors keep generating a sizable amount of data. Processing them efficiently as needed is pushing our understanding about large-scale data processing to its limits. Among many challenges data poses, a prominent one is storing the data and index- ing them so that scientist and researchers can come and ask for specific type of data collected at a given time and in a given region. -
Reflexión Académica En Diseño & Comunicación
ISSN 1668-1673 XXXII • 2017 Año XVIII. Vol 32. Noviembre 2017. Buenos Aires. Argentina Reflexión Académica en Diseño & Comunicación IV Congreso de Creatividad, Diseño y Comunicación para Profesores y Autoridades de Nivel Medio. `Interfaces Palermo´ Reflexión Académica en Diseño y Comunicación Comité Editorial Universidad de Palermo. Lucia Acar. Universidade Estácio de Sá. Brasil. Facultad de Diseño y Comunicación. Gonzalo Javier Alarcón Vital. Universidad Autónoma Metropolitana. México. Centro de Estudios en Diseño y Comunicación. Mercedes Alfonsín. Universidad de Buenos Aires. Argentina. Mario Bravo 1050. Fernando Alberto Alvarez Romero. Pontificia Universidad Católica del C1175ABT. Ciudad Autónoma de Buenos Aires, Argentina. Ecuador. Ecuador. www.palermo.edu Gonzalo Aranda Toro. Universidad Santo Tomás. Chile. [email protected] Christian Atance. Universidad de Lomas de Zamora. Argentina. Mónica Balabani. Universidad de Palermo. Argentina. Director Alberto Beckers Argomedo. Universidad Santo Tomás. Chile. Oscar Echevarría Renato Antonio Bertao. Universidade Positivo. Brasil. Allan Castelnuovo. Market Research Society. Reino Unido. Coordinadora de la Publicación Jorge Manuel Castro Falero. Universidad de la Empresa. Uruguay. Diana Divasto Raúl Castro Zuñeda. Universidad de Palermo. Argentina. Michael Dinwiddie. New York University. USA. Mario Rubén Dorochesi Fernandois. Universidad Técnica Federico Santa María. Chile. Adriana Inés Echeverria. Universidad de la Cuenca del Plata. Argentina. Universidad de Palermo Jimena Mariana García Ascolani. Universidad Comunera. Paraguay. Rector Marcelo Ghio. Instituto San Ignacio. Perú. Ricardo Popovsky Clara Lucia Grisales Montoya. Academia Superior de Artes. Colombia. Haenz Gutiérrez Quintana. Universidad Federal de Santa Catarina. Brasil. Facultad de Diseño y Comunicación José Korn Bruzzone. Universidad Tecnológica de Chile. Chile. Decano Zulema Marzorati. Universidad de Buenos Aires. Argentina. Oscar Echevarría Denisse Morales. -
Full-Graph-Limited-Mvn-Deps.Pdf
org.jboss.cl.jboss-cl-2.0.9.GA org.jboss.cl.jboss-cl-parent-2.2.1.GA org.jboss.cl.jboss-classloader-N/A org.jboss.cl.jboss-classloading-vfs-N/A org.jboss.cl.jboss-classloading-N/A org.primefaces.extensions.master-pom-1.0.0 org.sonatype.mercury.mercury-mp3-1.0-alpha-1 org.primefaces.themes.overcast-${primefaces.theme.version} org.primefaces.themes.dark-hive-${primefaces.theme.version}org.primefaces.themes.humanity-${primefaces.theme.version}org.primefaces.themes.le-frog-${primefaces.theme.version} org.primefaces.themes.south-street-${primefaces.theme.version}org.primefaces.themes.sunny-${primefaces.theme.version}org.primefaces.themes.hot-sneaks-${primefaces.theme.version}org.primefaces.themes.cupertino-${primefaces.theme.version} org.primefaces.themes.trontastic-${primefaces.theme.version}org.primefaces.themes.excite-bike-${primefaces.theme.version} org.apache.maven.mercury.mercury-external-N/A org.primefaces.themes.redmond-${primefaces.theme.version}org.primefaces.themes.afterwork-${primefaces.theme.version}org.primefaces.themes.glass-x-${primefaces.theme.version}org.primefaces.themes.home-${primefaces.theme.version} org.primefaces.themes.black-tie-${primefaces.theme.version}org.primefaces.themes.eggplant-${primefaces.theme.version} org.apache.maven.mercury.mercury-repo-remote-m2-N/Aorg.apache.maven.mercury.mercury-md-sat-N/A org.primefaces.themes.ui-lightness-${primefaces.theme.version}org.primefaces.themes.midnight-${primefaces.theme.version}org.primefaces.themes.mint-choc-${primefaces.theme.version}org.primefaces.themes.afternoon-${primefaces.theme.version}org.primefaces.themes.dot-luv-${primefaces.theme.version}org.primefaces.themes.smoothness-${primefaces.theme.version}org.primefaces.themes.swanky-purse-${primefaces.theme.version} -
Chris Mattmann
Chris Mattmann Associate Chief Technologist, NASA JPL Chris Mattmann is the Associate Chief Technologist and Innovation Officer in the Office of the Chief Technology and Innovation. Mattmann manages the IT Advanced Research and Open Source Projects Office and the NSF and Open Source Applications Office. Mattmann was formerly a member of the Engineering Administrative Office and formerly Chief Architect of the Instrument and Science Data Systems Section at NASA JPL with the responsibility for influencing science data system designs and facilitating the infusion of new technologies to meet our future challenges. Dr. Mattmann is also JPL's first Principal Scientist in the area of Data Science. He has over 18 years of experience at JPL and has conceived, realized and delivered the architecture for the next generation of reusable science data processing systems for NASA's Orbiting Carbon Observatory, NPP Sounder PEATE, and the Soil Moisture Active Passive (SMAP) Earth science missions. Mattmann's work has been funded by NASA, DARPA, DHS, NSF, NIH, NLM and by private industry. Mattmann was the first Vice President (VP) of Apache OODT (Object Oriented Data Technology), the first NASA project to enter the Apache Software Foundation (ASF) and he led the project's transition from JPL to the ASF. He contributes to open source as a Director at the Apache Software Foundation where he was one of the initial contributors to Apache Nutch as a member of its project management committee, the predecessor to the Apache Hadoop project. Mattmann is the progenitor of the Apache Tika framework, the digital "babel fish" and de-facto content analysis and detection framework that exists. -
PDF Download Scaling Big Data with Hadoop and Solr
SCALING BIG DATA WITH HADOOP AND SOLR - PDF, EPUB, EBOOK Hrishikesh Vijay Karambelkar | 166 pages | 30 Apr 2015 | Packt Publishing Limited | 9781783553396 | English | Birmingham, United Kingdom Scaling Big Data with Hadoop and Solr - PDF Book The default duration between two heartbeats is 3 seconds. Some other SQL-based distributed query engines to certainly bear in mind and consider for your use cases are:. What Can We Help With? Check out some of the job opportunities currently listed that match the professional profile, many of which seek experience with Search and Solr. This mode can be turned off manually by running the following command:. Has the notion of parent-child document relationships These exist as separate documents within the index, limiting their aggregation functionality in deeply- nested data structures. This step will actually create an authorization key with ssh, bypassing the passphrase check as shown in the following screenshot:. Fields may be split into individual tokens and indexed separately. Any key starting with a will go in the first region, with c the third region and z the last region. Now comes the interesting part. After the jobs are complete, the results are returned to the remote client via HiveServer2. Finally, Hadoop can accept data in just about any format, which eliminates much of the data transformation involved with the data processing. The difference in ingestion performance between Solr and Rocana Search is striking. Aptude has been working with our team for the past four years and we continue to use them and are satisfied with their work Warren E. These tables support most of the common data types that you know from the relational database world. -
Programming Hive
www.it-ebooks.info www.it-ebooks.info Programming Hive Edward Capriolo, Dean Wampler, and Jason Rutherglen Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo www.it-ebooks.info Programming Hive by Edward Capriolo, Dean Wampler, and Jason Rutherglen Copyright © 2012 Edward Capriolo, Aspect Research Associates, and Jason Rutherglen. All rights re- served. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editors: Mike Loukides and Courtney Nash Indexer: Bob Pfahler Production Editors: Iris Febres and Rachel Steely Cover Designer: Karen Montgomery Proofreaders: Stacie Arellano and Kiel Van Horn Interior Designer: David Futato Illustrator: Rebecca Demarest October 2012: First Edition. Revision History for the First Edition: 2012-09-17 First release See http://oreilly.com/catalog/errata.csp?isbn=9781449319335 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Programming Hive, the image of a hornet’s hive, and related trade dress are trade- marks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information con- tained herein. -
Classifying, Evaluating and Advancing Big Data Benchmarks
Classifying, Evaluating and Advancing Big Data Benchmarks Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften vorgelegt beim Fachbereich 12 Informatik der Johann Wolfgang Goethe-Universität in Frankfurt am Main von Todor Ivanov aus Stara Zagora Frankfurt am Main 2019 (D 30) vom Fachbereich 12 Informatik der Johann Wolfgang Goethe-Universität als Dissertation angenommen. Dekan: Prof. Dr. Andreas Bernig Gutachter: Prof. Dott. -Ing. Roberto V. Zicari Prof. Dr. Carsten Binnig Datum der Disputation: 23.07.2019 Abstract The main contribution of the thesis is in helping to understand which software system parameters mostly affect the performance of Big Data Platforms under realistic workloads. In detail, the main research contributions of the thesis are: 1. Definition of the new concept of heterogeneity for Big Data Architectures (Chapter 2); 2. Investigation of the performance of Big Data systems (e.g. Hadoop) in virtual- ized environments (Section 3.1); 3. Investigation of the performance of NoSQL databases versus Hadoop distribu- tions (Section 3.2); 4. Execution and evaluation of the TPCx-HS benchmark (Section 3.3); 5. Evaluation and comparison of Hive and Spark SQL engines using benchmark queries (Section 3.4); 6. Evaluation of the impact of compression techniques on SQL-on-Hadoop engine performance (Section 3.5); 7. Extensions of the standardized Big Data benchmark BigBench (TPCx-BB) (Section 4.1 and 4.3); 8. Definition of a new benchmark, called ABench (Big Data Architecture Stack Benchmark), that takes into account the heterogeneity of Big Data architectures (Section 4.5). The thesis is an attempt to re-define system benchmarking taking into account the new requirements posed by the Big Data applications.