Projects – Other Than Hadoop! Created By:-Samarjit Mahapatra [email protected]
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Horn: a System for Parallel Training and Regularizing of Large-Scale Neural Networks
Horn: A System for Parallel Training and Regularizing of Large-Scale Neural Networks Edward J. Yoon [email protected] I Am ● Edward J. Yoon ● Member and Vice President of Apache Software Foundation ● Committer, PMC, Mentor of ○ Apache Hama ○ Apache Bigtop ○ Apache Rya ○ Apache Horn ○ Apache MRQL ● Keywords: big data, cloud, machine learning, database What is Apache Software Foundation? The Apache Software Foundation is an Non-profit foundation that is dedicated to open source software development 1) What Apache Software Foundation is, 2) Which projects are being developed, 3) What’s HORN? 4) and How to contribute them. Apache HTTP Server (NCSA HTTPd) powers nearly 500+ million websites (There are 644 million websites on the Internet) And Now! 161 Top Level Projects, 108 SubProjects, 39 Incubating Podlings, 4700+ Committers, 550 ASF Members Unknown number of developers and users Domain Diversity Programming Language Diversity Which projects are being developed? What’s HORN? ● Oct 2015, accepted as Apache Incubator Project ● Was born from Apache Hama ● A System for Deep Neural Networks ○ A neuron-level abstraction framework ○ Written in Java :/ ○ Works on distributed environments Apache Hama 1. K-means clustering Hama is 1,000x faster than Apache Mahout At UT Arlington & Oracle 2013 2. PageRank on 10 Billion edges Graph Hama is 3x faster than Facebook’s Giraph At Samsung Electronics (Yoon & Kim) 2015 3. Top-k Set Similarity Joins on Flickr Hama is clearly faster than Apache Spark At IEEE 2015 (University of Melbourne) Why we do this? 1. How to parallelize the training of large models? 2. How to avoid overfitting due to large size of the network, even with large datasets? JonathanNet Distributed Training Parameter Server Parameter Server Parameter Swapping Task 5 Each group performs Task 2 Task 4 Task 3 .. -
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. -
The Network Data Storage Model Based on the HTML
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) The network data storage model based on the HTML Lei Li1, Jianping Zhou2 Runhua Wang3, Yi Tang4 1Network Information Center 3Teaching Quality and Assessment Office Chongqing University of Science and Technology Chongqing University of Science and Technology Chongqing, China Chongqing, China [email protected] [email protected] 2Network Information Center 4School of Electronic and Information Engineering Chongqing University of Science and Technology Chongqing University of Science and Technology Chongqing, China Chongqing, China [email protected] [email protected] Abstract—in this paper, the mass oriented HTML data refers popular NoSQL for HTML database. This article describes to those large enough for HTML data, that can no longer use the SMAQ stack model and those who today may be traditional methods of treatment. In the past, has been the included in the model under mass oriented HTML data Web search engine creators have to face this problem be the processing tools. first to bear the brunt. Today, various social networks, mobile applications and various sensors and scientific fields are II. MAPREDUCE created daily with PB for HTML data. In order to deal with MapReduce is a Google for creating web webpage index the massive data processing challenges for HTML, Google created MapReduce. Google and Yahoo created Hadoop created. MapReduce framework has become today most hatching out of an intact mass oriented HTML data processing mass oriented HTML data processing plant. MapReduce is tools for ecological system. the key, will be oriented in the HTML data collection of a query division, then at multiple nodes to execute in parallel. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Optimizing Resource Utilization in Distributed Computing Systems For
THESE` DE DOCTORAT DE L’ETABLISSEMENT´ UNIVERSITE´ BOURGOGNE FRANCHE-COMTE´ PREPAR´ EE´ A` L’UNIVERSITE´ DE FRANCHE-COMTE´ Ecole´ doctorale n°37 Sciences Pour l’Ingenieur´ et Microtechniques Doctorat d’Informatique par ANTHONY NASSAR Optimizing Resource Utilization in Distributed Computing Systems for Automotive Applications Optimisation de l’utilisation des ressources dans les systemes` informatiques distribues´ pour les applications automobiles These` present´ ee´ et soutenue publiquement le 04-02-2021 a` Belfort, devant le Jury compose´ de : MR CERIN CHRISTOPHE Professeur a` l’Universite´ Sorbonne Paris Nord President´ MR CHBEIR RICHARD Professeur a` l’Universite´ de Pau et des Pays de l’Adour Rapporteur MME BENBERNOU SALIMA Professeur a` l’Universite´ Paris-Descartes Rapporteur MR MOSTEFAOUI AHMED Maˆıtre de conferences´ a` l’Universite´ de Franche-Comte´ Directeur de these` MR DESSABLES FRANC¸ OIS Ingenieur´ chez Groupe PSA Codirecteur de these` DOCTORAL THESIS OF THE UNIVERSITY BOURGOGNE FRANCHE-COMTE´ INSTITUTION PREPARED AT UNIVERSITE´ DE FRANCHE-COMTE´ Doctoral school n°37 Engineering Sciences and Microtechnologies Computer Science Doctorate by ANTHONY NASSAR Optimizing Resource Utilization in Distributed Computing Systems for Automotive Applications Optimisation de l’utilisation des ressources dans les systemes` informatiques distribues´ pour les applications automobiles Thesis presented and publicly defended in Belfort, on 04-02-2021 Composition of the Jury : CERIN CHRISTOPHE Professor at Universite´ Sorbonne Paris Nord President -
Graft: a Debugging Tool for Apache Giraph
Graft: A Debugging Tool For Apache Giraph Semih Salihoglu, Jaeho Shin, Vikesh Khanna, Ba Quan Truong, Jennifer Widom Stanford University {semih, jaeho.shin, vikesh, bqtruong, widom}@cs.stanford.edu ABSTRACT optional master.compute() function is executed by the Master We address the problem of debugging programs written for Pregel- task between supersteps. like systems. After interviewing Giraph and GPS users, we devel- We have tackled the challenge of debugging programs written oped Graft. Graft supports the debugging cycle that users typically for Pregel-like systems. Despite being a core component of pro- go through: (1) Users describe programmatically the set of vertices grammers’ development cycles, very little work has been done on they are interested in inspecting. During execution, Graft captures debugging in these systems. We interviewed several Giraph and the context information of these vertices across supersteps. (2) Us- GPS programmers (hereafter referred to as “users”) and studied vertex.compute() ing Graft’s GUI, users visualize how the values and messages of the how they currently debug their functions. captured vertices change from superstep to superstep,narrowing in We found that the following three steps were common across users: suspicious vertices and supersteps. (3) Users replay the exact lines (1) Users add print statements to their code to capture information of the vertex.compute() function that executed for the sus- about a select set of potentially “buggy” vertices, e.g., vertices that picious vertices and supersteps, by copying code that Graft gener- are assigned incorrect values, send incorrect messages, or throw ates into their development environments’ line-by-line debuggers. -
Big Data: a Survey the New Paradigms, Methodologies and Tools
Big Data: A Survey The New Paradigms, Methodologies and Tools Enrico Giacinto Caldarola1,2 and Antonio Maria Rinaldi1,3 1Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, Italy 2Institute of Industrial Technologies and Automation, National Research Council, Bari, Italy 3IKNOS-LAB Intelligent and Knowledge Systems, University of Naples Federico II, LUPT 80134, via Toledo, 402-Napoli, Italy Keywords: Big Data, NoSQL, NewSQL, Big Data Analytics, Data Management, Map-reduce. Abstract: For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features. -
A Review on Big Data Analytics in the Field of Agriculture
International Journal of Latest Transactions in Engineering and Science A Review on Big Data Analytics in the field of Agriculture Harish Kumar M Department. of Computer Science and Engineering Adhiyamaan College of Engineering,Hosur, Tamilnadu, India Dr. T Menakadevi Dept. of Electronics and Communication Engineering Adhiyamaan College of Engineering,Hosur, Tamilnadu, India Abstract- Big Data Analytics is a Data-Driven technology useful in generating significant productivity improvement in various industries by collecting, storing, managing, processing and analyzing various kinds of structured and unstructured data. The role of big data in Agriculture provides an opportunity to increase economic gain of the farmers by undergoing digital revolution in this aspect we examine through precision agriculture schemas equipped in many countries. This paper reviews the applications of big data to support agriculture. In addition it attempts to identify the tools that support the implementation of big data applications for agriculture services. The review reveals that several opportunities are available for utilizing big data in agriculture; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology. Keywords—Agriculture, Big data Analytics, Hadoop, HDFS, Farmers I. INTRODUCTION The technologies employed are exciting, involve analysis of mind-numbing amounts of data and require fundamental rethinking as to what constitutes data. Big data is a collecting raw data which undergoes various phases like Classification, Processing and organizing into meaningful information. Raw information cannot be consumed directly for any form of analysis. It’s a process of examining uncover patterns, finding unknown correlation and finding useful information which are adopted for decision making analysis. -
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} -
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. -
Big Data Solutions and Applications
P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management.