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BEYONDMR 2016 : 3rd Workshop on Algorithms and Systems for MapReduce and Beyond

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Link: http://sites.google.com/site/beyondmr2016/
 
When Jul 1, 2016 - Jul 1, 2016
Where San Francisco, USA
Submission Deadline Mar 5, 2016
Notification Due Apr 11, 2016
Final Version Due May 1, 2016
 

Call For Papers


BEYONDMR'16
3rd Workshop on Algorithms and Systems for MapReduce and Beyond, July 1, 2016.
https://sites.google.com/site/beyondmr2016/

Held in conjunction with SIGMOD 2016
San Francisco, USA, June 26th - July 1st, 2016
http://sigmod2016.org/

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WORKSHOP FOCUS
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The third BeyondMR workshop aims to explore algorithms, computational models, architectures, languages and interfaces for systems that need large-scale parallelization and systems designed to support efficient parallelization and fault tolerance. These include specialized programming and data-management systems based on MapReduce and extensions, graph processing systems, data-intensive workflow and dataflow systems.

We invite submissions on topics such as

Frameworks for Large-Scale Analytical Processing:
- Models, architectures and languages for data processing pipelines, data-intensive workflows, DAGs of operations/MapReduce jobs, dataflows, and data-mashups.
- Extensions of MapReduce with more fundamental functions other than Map and Reduce and more complex dataflow connections between function inputs and outputs.
- Expressing and parallelising iterations, incremental iterations, and programs consisting of large DAGs of operations.
- Approaches to achieving fault tolerance and to recovering from failures.

Algorithms for Large-Scale Data Processing:
- Methods and techniques for designing efficient algorithms for MapReduce and similar systems.
- Experiments and experience with new algorithms in these settings.

Cost Models and Optimization Techniques:
- Formal definitions of models that evaluate the efficiency of algorithms in large-scale parallel processing systems taking into account the requirements of such systems in different applications.
- Testing and benchmarking of MapReduce extensions and data-intensive workflows.

Resource Management for Many-Task Computing:
- Scheduling of tasks and load-balancing techniques.
- Methods to tackle data skewness.
- Study of cases where automatic data distribution in MapReduce and similar systems does not provide sufficient data balancing.
- Design of algorithms that avoid skewness.
- Extensions of MapReduce that automatically tackle data skewness.

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IMPORTANT DATES
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Papers submission deadline: Sun March 5, 2016
Authors notification: Sun April 11, 2016
Deadline for camera-ready copy: Sun May 1, 2016
Workshop: Fri July 1, 2016

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SUBMISSION GUIDELINES
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We invite full research or experience papers (up to 10 pages), or short papers (up to 4 pages) describing research in progress, formatted using the ACM double-column style (http://conferences.sigcomm.org/imc/2009/sig-alternate-10pt.cls)

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PUBLICATION
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The workshop proceedings will be published in ACM DL and the organizers will prepare a SIGMOD Record report.

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ORGANIZERS
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Foto Afrati (National Technical University of Athens, Greece)
Jan Hidders (TU Delft, The Netherlands)
Christopher Re (Stanford, USA)
Jacek Sroka (University of Warsaw, Poland)
Jeffrey Ullman (Stanford University)

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Program Committee (in progress)
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– Chris Re, Stanford University (PC chair)
– Foto Afrati, National Technical University of Athens
– Jeffrey Ullman, Stanford University
– Jacek Sroka, University of Warsaw
– Jan Hidders, Delft University of Technology
– Zhengkui Wang, Singapore Institute of Technology
– Khalid Belhajjame, PSL, Universite Paris-Dauphine, LAMSADE
– Sourav Bhowmick, Nanyang Technological University
– Graham Cormode, University of Warwick
– Asterios Katsifodimos, Technical University of Berlin
– Paris Koutris, University of Washington
– Dionysios Logothetis, Facebook
– Frank McSherry, ETH Zurich
– Krzysztof Onak, IBM Research
– Mark Santcroos, Rutgers University
– Gautam Shroff, Tata Consultancy Services RD
– Dan Suciu, University of Washington
– Jianwu Wang, San Diego Supercomputer Center, University of California, San Diego
– Tim Kraska, Brown University
– Krzysztof Rzadca, University of Warsaw
– Semih Salihoglu, Stanford University
- Ulf Leser, Humboldt-Universität zu Berlin
- Fabio Porto, National Laboratory of Scientific Computation, Brasil
- Eiko Yoneki, University of Cambridge
- Umut Acar, Carnegie Mellon University
- Daniel De Oliveira, Fluminense Federal University
- Tamer Özsu, University of Waterloo
- Anthony Tung, National University of Singapore
- Sergei Vassilvitskii, Google
- Yogesh Simmhan, Indian Institute of Science, Bangalore

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