YARN Essentials Table of Contents
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Large-Scale Learning from Data Streams with Apache SAMOA
Large-Scale Learning from Data Streams with Apache SAMOA Nicolas Kourtellis1, Gianmarco De Francisci Morales2, and Albert Bifet3 1 Telefonica Research, Spain, [email protected] 2 Qatar Computing Research Institute, Qatar, [email protected] 3 LTCI, Télécom ParisTech, France, [email protected] Abstract. Apache SAMOA (Scalable Advanced Massive Online Anal- ysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical soft- ware tools to capture, store, manage, and analyze, due to the time and memory complexity. Apache SAMOA provides a collection of dis- tributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It fea- tures a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, and Apache Samza. Apache SAMOA is written in Java and is available at https://samoa.incubator.apache.org under the Apache Software Li- cense version 2.0. 1 Introduction Big data are “data whose characteristics force us to look beyond the traditional methods that are prevalent at the time” [18]. For instance, social media are one of the largest and most dynamic sources of data. These data are not only very large due to their fine grain, but also being produced continuously. Furthermore, such data are nowadays produced by users in different environments and via a multitude of devices. For these reasons, data from social media and ubiquitous environments are perfect examples of the challenges posed by big data. -
DSP Frameworks DSP Frameworks We Consider
Università degli Studi di Roma “Tor Vergata” Dipartimento di Ingegneria Civile e Ingegneria Informatica DSP Frameworks Corso di Sistemi e Architetture per Big Data A.A. 2017/18 Valeria Cardellini DSP frameworks we consider • Apache Storm (with lab) • Twitter Heron – From Twitter as Storm and compatible with Storm • Apache Spark Streaming (lab) – Reduce the size of each stream and process streams of data (micro-batch processing) • Apache Flink • Apache Samza • Cloud-based frameworks – Google Cloud Dataflow – Amazon Kinesis Streams Valeria Cardellini - SABD 2017/18 1 Apache Storm • Apache Storm – Open-source, real-time, scalable streaming system – Provides an abstraction layer to execute DSP applications – Initially developed by Twitter • Topology – DAG of spouts (sources of streams) and bolts (operators and data sinks) Valeria Cardellini - SABD 2017/18 2 Stream grouping in Storm • Data parallelism in Storm: how are streams partitioned among multiple tasks (threads of execution)? • Shuffle grouping – Randomly partitions the tuples • Field grouping – Hashes on a subset of the tuple attributes Valeria Cardellini - SABD 2017/18 3 Stream grouping in Storm • All grouping (i.e., broadcast) – Replicates the entire stream to all the consumer tasks • Global grouping – Sends the entire stream to a single task of a bolt • Direct grouping – The producer of the tuple decides which task of the consumer will receive this tuple Valeria Cardellini - SABD 2017/18 4 Storm architecture • Master-worker architecture Valeria Cardellini - SABD 2017/18 5 Storm -
Comparative Analysis of Data Stream Processing Systems
Shah Zeb Mian Comparative Analysis of Data Stream Processing Systems Master’s Thesis in Information Technology February 23, 2020 University of Jyväskylä Faculty of Information Technology Author: Shah Zeb Mian Contact information: [email protected] Supervisors: Oleksiy Khriyenko, and Vagan Terziyan Title: Comparative Analysis of Data Stream Processing Systems Työn nimi: Vertaileva analyysi Data Stream-käsittelyjärjestelmistä Project: Master’s Thesis Study line: All study lines Page count: 48+0 Abstract: Big data processing systems are evolving to be more stream oriented where data is processed continuously by processing it as soon as it arrives. Earlier data was often stored in a database, a file system or other form of data storage system. Applications would query the data as needed. Stram processing is the processing of data in motion. It works on continuous data retrieved from different resources. Instead of periodically collecting huge static data, streaming frameworks process data as soon as it becomes available, hence reducing latency. This thesis aims to conduct a comparative analysis of different streaming processors based on selected features. Research focuses on Apache Samza, Apache Flink, Apache Storm and Apache Spark Structured Streaming. Also, this thesis explains Apache Kafka which is a log-based data storage widely used in streaming frameworks. Keywords: Big Data, Stream Processing,Batch Processing,Streaming Engines, Apache Kafka, Apache Samza Suomenkielinen tiivistelmä: Big data-käsittelyjärjestelmät ovat tällä hetkellä kehittymässä stream-orientoituneiksi, eli data käsitellään heti saapuessaan. Perinteisemmin data säilöt- tiin tietokantaan, tiedostopohjaisesti tai muuhun tiedonsäilytysjärjestelmään, ja applikaatiot hakivat datan tarvittaessa. Stream-pohjainen järjestelmä käsittelee liikkuvaa dataa, jatkuva- aikaista dataa useasta lähteestä. Sen sijaan, että haetaan ajoittain dataa, stream-pohjaiset frameworkit pystyvät käsittelemään i dataa heti kun se on saatavilla, täten vähentäen viivettä. -
Network Traffic Profiling and Anomaly Detection for Cyber Security
Network traffic profiling and anomaly detection for cyber security Laurens D’hooge Student number: 01309688 Supervisors: Prof. dr. ir. Filip De Turck, dr. ir. Tim Wauters Counselors: Prof. dr. Bruno Volckaert, dr. ir. Tim Wauters A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in Information Engineering Technology Academic year: 2017-2018 Acknowledgements This thesis is the result of 4 months work and I would like to express my gratitude towards the people who have guided me throughout this process. First and foremost I’d like to thank my thesis advisors prof. dr. Bruno Volckaert and dr. ir. Tim Wauters. By virtue of their knowledge and clear communication, I was able to maintain a clear target. Secondly I would like to thank prof. dr. ir. Filip De Turck for providing me the opportunity to conduct research in this field with the IDLab research group. Special thanks to Andres Felipe Ocampo Palacio and dr. Marleen Denert are in order as well. Mr. Ocampo’s Phd research into big data processing for network traffic and the resulting framework are an integral part of this thesis. Ms. Denert has been the go-to member of the faculty staff for general advice and administrative dealings. The final token of gratitude I’d like to extend to my family and friends for their continued support during this process. Laurens D’hooge Network traffic profiling and anomaly detection for cyber security Laurens D’hooge Supervisor(s): prof. dr. ir. Filip De Turck, dr. ir. Tim Wauters Abstract— This article is a short summary of the research findings of a creation of APT2. -
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. -
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. -
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. -
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. -
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} -
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.