MapReduce Workshop

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                            CALL FOR PAPERS

                   The Third International Workshop on 

             MapReduce and its Applications (MAPREDUCE'12)

          June 18-19, 2012 HPDC'2012, Delft, the Netherlands.
	        (http://graal.ens-lyon.fr/mapreduce/)

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SCOPE

Since its introduction in 2004 by Google, MapReduce has become the programming model of choice for processing large data sets. MapReduce borrows from functional programming, where a programmer can define both a Map task that maps a data set into another data set, and a Reduce task that combines intermediate outputs into a final result. Although MapReduce was originally developed for use by web enterprises in large data-centers, this technique has gained a lot of attention from the scientific community for its applicability in large parallel data analysis (including geographic, high energy physics, genomics, etc..).

The purpose of the workshop is to provide a forum for discussing recent advances, identifying open issues, introducing developments and tools, and presenting applications and enhancements for MapReduce (or very similar) systems. We therefore cordially invite contributions that investigate these issues, introduce new execution environments, apply performance evaluations and show the applicability to science and enterprise applications.

PREVIOUS EDITION

In 2011, the workshop had around 20 submissions and accepted 9 of them. All the HPDC workshop papers were published in a separate workshop proceeding. The papers are available online through the ACM web site, and the program for the 2011 edition can be found here :

http://graal.ens-lyon.fr/~gfedak/mapreduce-2011/

Gabriel Antoniu, from INRIA, gave the keynote speech.

TOPICS OF INTEREST

    * MapReduce implementation issues and improvements
    * Implementation optimization for GPU and multi-core systems
    * Extensions to the programing model
    * Large-scale MapReduce (Grid and Desktop Grid)
    * Use of CDN and P2P techniques
    * Heterogeneity and fault-tolerance
    * Scientific data-sets analysis
    * Data and compute-intensive applications
    * Tools and environments for MapReduce
    * Algorithms using the MapReduce paradigm

PAPER SUBMISSIONS

Authors are invited to submit full papers of at most 8 pages, including all figures and references. Papers should be formatted in the ACM proceedings style (e.g., http://www.acm.org/sigs/publications/proceedings-templates). Submitted papers must be original work that has not appeared in and is not under consideration for another conference or a journal. Accepted papers will be published by ACM in the conference workshops proceedings.

Papers should be submitted here: TO BE ANNOUNCED

IMPORTANT DATES

    * Manuscript submission deadline : February 25, 2012
    * Acceptance notification : March 26, 2012
    * Camera-ready paper deadline : April 16, 2012
    * Workshop dates : June 18-19, 2012

ORGANIZATION COMMITTEE

General Chairs

   Gilles Fedak, INRIA/LIP (contact: gilles.fedak@inria.fr)
   Geoffrey Fox, Indiana University (contact: gcf@cs.indiana.edu)

Program Chair

    Simon Delamare, INRIA/LIP (contact: simon.delamare@inria.fr)

Publicity chair

   Haiwu He, INRIA/LIP (contact: haiwu.he@inria.fr)

PROGRAM COMMITTEE

    * Alexandre de Assis Bento Lima, Federal University of Rio de Janeiro
    * Amund Tveit, Atbrox      
    * Carlo Mastroianni, ICAR-CNR  
    * Christian Engelmann, Oak Ridge National Laboratory  
    * Francisco V. Brasileiro, Federal University of Campina Grande
    * Frédéric Suter, IN2P3/CNRS
    * Gabriel Antoniu, INRIA
    * Heithem Abbes, Faculty of Sciences of Tunis
    * Heshan Lin, Virginia Polytechnic Institute and State University 
    * Hidemoto Nakada, AIST
    * Jerry Zhao, Google
    * José A.B. Fortes, University of Florida
    * Judy Qiu, Indiana University
    * Michael C. Schatz, Cold Spring Harbor Laboratory
    * Oleg Lodygensky, CNRS
    * Reza Akbarinia, INRIA
    * Shantenu Jha, Louisiana State University
    * Xuanhuan Shi, Huazhong University of Science and Technology
    * Yang Yang, Netflix