S.Sathya M.Victor Jose Department of Computer Science and Engineer Noorul Islam Centre for Higher Education Kumaracoil,Tamilnadu,IndiaPROCEEDINGS OF ICETECT.

Slides:



Advertisements
Similar presentations
Abstraction Layers Why do we need them? –Protection against change Where in the hourglass do we put them? –Computer Scientist perspective Expose low-level.
Advertisements

Duagi Bulent UNIVERSITY POLITEHNICA of BUCHAREST DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY POLITEHNICA of BUCHAREST DEPARTMENT OF COMPUTER SCIENCE.
Achieving Elasticity for Cloud MapReduce Jobs Khaled Salah IEEE CloudNet 2013 – San Francisco November 13, 2013.
LIBRA: Lightweight Data Skew Mitigation in MapReduce
Clouds from FutureGrid’s Perspective April Geoffrey Fox Director, Digital Science Center, Pervasive.
June 22-23, 2005 Technology Infusion Team Committee1 High Performance Parallel Lucene search (for an OAI federation) K. Maly, and M. Zubair Department.
Institute for Software Science – University of ViennaP.Brezany 1 Databases and the Grid Peter Brezany Institute für Scientific Computing University of.
Authors: Thilina Gunarathne, Tak-Lon Wu, Judy Qiu, Geoffrey Fox Publish: HPDC'10, June 20–25, 2010, Chicago, Illinois, USA ACM Speaker: Jia Bao Lin.
Evaluation of distributed open source solutions in CERN database use cases HEPiX, spring 2015 Kacper Surdy IT-DB-DBF M. Grzybek, D. L. Garcia, Z. Baranowski,
An Agent-Oriented Approach to the Integration of Information Sources Michael Christoffel Institute for Program Structures and Data Organization, University.
Daniel Abadi Yale University. * The Big Data phenomenon is the best thing that could have happened to the database community * Despite other definitions.
TECHNIQUES FOR OPTIMIZING THE QUERY PERFORMANCE OF DISTRIBUTED XML DATABASE - NAHID NEGAR.
Project Proposal (Title + Abstract) Due Wednesday, September 4, 2013.
資訊工程系智慧型系統實驗室 iLab 南台科技大學 1 Optimizing Cloud MapReduce for Processing Stream Data using Pipelining 出處 : 2011 UKSim 5th European Symposium on Computer Modeling.
Meta-MapReduce A Technique for Reducing Communication in MapReduce Computations Foto N. Afrati 1, Shlomi Dolev 2, Shantanu Sharma 2, and Jeffrey D. Ullman.
Software Architecture
Yongzhi Wang, Jinpeng Wei VIAF: Verification-based Integrity Assurance Framework for MapReduce.
CS525: Special Topics in DBs Large-Scale Data Management Hadoop/MapReduce Computing Paradigm Spring 2013 WPI, Mohamed Eltabakh 1.
Presented by CH.Anusha.  Apache Hadoop framework  HDFS and MapReduce  Hadoop distributed file system  JobTracker and TaskTracker  Apache Hadoop NextGen.
A Metadata Based Approach For Supporting Subsetting Queries Over Parallel HDF5 Datasets Vignesh Santhanagopalan Graduate Student Department Of CSE.
Introduction to Apache Hadoop Zibo Wang. Introduction  What is Apache Hadoop?  Apache Hadoop is a software framework which provides open source libraries.
Hadoop/MapReduce Computing Paradigm 1 Shirish Agale.
Introduction to Hadoop and HDFS
Cloud Distributed Computing Platform 2 Content of this lecture is primarily from the book “Hadoop, The Definite Guide 2/e)
CSE 548 Advanced Computer Network Security Document Search in MobiCloud using Hadoop Framework Sayan Cole Jaya Chakladar Group No: 1.
Optimizing Cloud MapReduce for Processing Stream Data using Pipelining 作者 :Rutvik Karve , Devendra Dahiphale , Amit Chhajer 報告 : 饒展榕.
MARISSA: MApReduce Implementation for Streaming Science Applications 作者 : Fadika, Z. ; Hartog, J. ; Govindaraju, M. ; Ramakrishnan, L. ; Gunter, D. ; Canon,
Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters Hung-chih Yang(Yahoo!), Ali Dasdan(Yahoo!), Ruey-Lung Hsiao(UCLA), D. Stott Parker(UCLA)
Mining High Utility Itemset in Big Data
1 Scalable Exploratory Data Mining of Distributed Geoscientific Data Authors : E.C Shek, R.R Muntz, E. Mesrobian and K. Ng by Sona Srinivasan.
Design of a Search Engine for Metadata Search Based on Metalogy Ing-Xiang Chen, Che-Min Chen,and Cheng-Zen Yang Dept. of Computer Engineering and Science.
Grid Computing at Yahoo! Sameer Paranjpye Mahadev Konar Yahoo!
Optimizing Cloud MapReduce for Processing Stream Data using Pipelining 2011 UKSim 5th European Symposium on Computer Modeling and Simulation Speker : Hong-Ji.
Department of Computer Science MapReduce for the Cell B. E. Architecture Marc de Kruijf University of Wisconsin−Madison Advised by Professor Sankaralingam.
Virtualization and Databases Ashraf Aboulnaga University of Waterloo.
CS525: Big Data Analytics MapReduce Computing Paradigm & Apache Hadoop Open Source Fall 2013 Elke A. Rundensteiner 1.
DynamicMR: A Dynamic Slot Allocation Optimization Framework for MapReduce Clusters Nanyang Technological University Shanjiang Tang, Bu-Sung Lee, Bingsheng.
Map-Reduce examples 1. So, what is it? A two phase process geared toward optimizing broad, widely distributed parallel computing platforms Apache Hadoop.
Web Log Data Analytics with Hadoop
Impala. Impala: Goals General-purpose SQL query engine for Hadoop High performance – C++ implementation – runtime code generation (using LLVM) – direct.
A N I N - MEMORY F RAMEWORK FOR E XTENDED M AP R EDUCE 2011 Third IEEE International Conference on Coud Computing Technology and Science.
Fire Emissions Network Sept. 4, 2002 A white paper for the development of a NSF Digital Government Program proposal Stefan Falke Washington University.
Hadoop/MapReduce Computing Paradigm 1 CS525: Special Topics in DBs Large-Scale Data Management Presented By Kelly Technologies
A Two-phase Execution Engine of Reduce Tasks In Hadoop MapReduce XiaohongZhang*GuoweiWang* ZijingYang*YangDing School of Computer Science and Technology.
2011 International Symposium on Intelligence Information Processing and Trusted Computing Huanggang Normal University Hubei, China Gaizhen Yang Speaker.
Computer Vision Group Department of Computer Science University of Illinois at Urbana-Champaign.
{ Tanya Chaturvedi MBA(ISM) Hadoop is a software framework for distributed processing of large datasets across large clusters of computers.
REX: RECURSIVE, DELTA-BASED DATA-CENTRIC COMPUTATION Yavuz MESTER Svilen R. Mihaylov, Zachary G. Ives, Sudipto Guha University of Pennsylvania.
By Shivaraman Janakiraman, Magesh Khanna Vadivelu.
B ig D ata Analysis for Page Ranking using Map/Reduce R.Renuka, R.Vidhya Priya, III B.Sc., IT, The S.F.R.College for Women, Sivakasi.
Resource Selection in Grids Using Contract Net Kunal Goswami, Arobinda Gupta Cisco Systems, Bangalore, India Dept. of Computer Science & Engineering and.
System Software Laboratory Databases and the Grid by Paul Watson University of Newcastle Grid Computing: Making the Global Infrastructure a Reality June.
MapReduce Compilers-Apache Pig
”Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters” Published In SIGMOD '07 By Yahoo! Senthil Nathan N IIT Bombay.
Sushant Ahuja, Cassio Cristovao, Sameep Mohta
Condor – A Hunter of Idle Workstation
Map Reduce.
Distributed Operating Systems
DUCKS – Distributed User-mode Chirp-Knowledgeable Server
Distributed Shared Memory
Cloud Distributed Computing Environment Hadoop
Research Issues in Electronic Commerce
MapReduce: Data Distribution for Reduce
February 26th – Map/Reduce
Cse 344 May 4th – Map/Reduce.
Spark and Scala.
Interpret the execution mode of SQL query in F1 Query paper
Task-Farm Distributed Computing
MAPREDUCE TYPES, FORMATS AND FEATURES
Introduction to Spark.
Presentation transcript:

S.Sathya M.Victor Jose Department of Computer Science and Engineer Noorul Islam Centre for Higher Education Kumaracoil,Tamilnadu,IndiaPROCEEDINGS OF ICETECT 2011 Report : Chang, Kun-Hsiang

Outline Abstract VIRTUAL DATABASE SYSTEM IMPLEMENTATION OF VIRTUAL DATABASE WITH HADOOP MAPREDUCE QUERY OPTIMIZATION

Abstract proposes to utilize the parallel and distributed processing capability of Hadoop MapReduce for handling heterogeneous query execution on large datasets.

VIRTUAL DATABASE SYSTEM Publisher Mapper Executor Wrapper Metadata

VIRTUAL DATABASE SYSTEM

IMPLEMENTATION OF VIRTUAL DATABASE WITH HADOOP MAPREDUCE

QUERY OPTIMIZATION Removes the grouped queries from this queue and Processes using different workers. This effectively optimizes the time spent in Processing and executing duplicate queries.

Report : Chang, Kun-Hsiang