[hpc-announce] CFP for the BeyondMR workshop (Algorithms and Systems for MapReduce and Beyond) at SIGMOD 2017
Jan Hidders
jan.hidders at vub.ac.be
Sun Oct 16 09:39:37 CDT 2016
[Apologies of you are receiving this CFP multiple times]
* Call for papers *
BEYONDMR'17
The 4th Workshop on Algorithms and Systems for MapReduce and Beyond, May 19, 2017.
https://sites.google.com/site/beyondmr2017/ <https://sites.google.com/site/beyondmr2017/>
Held in conjunction with SIGMOD 2017
Raleigh, NC, USA, May 14-19, 2017
http://sigmod2017.org/ <http://sigmod2017.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, networks of operations/MapReduce jobs, dataflows,
and data-mashups.
- Analysis of programs for workflow systems, e.g., Spark.
- Expressing and parallelising iterations, incremental iterations, and
programs consisting of large networks 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.
- Study of cases where automatic data distribution in MapReduce and
similar systems does not provide sufficient data balancing.
- Algorithms, methods and frameworks to address data skewness.
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IMPORTANT DATES
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Papers submission deadline: Wed Jan 27, 2017
Authors notification: Sun March 5, 2017
Deadline for camera-ready copy: Sun March 19, 2017
Workshop: Fri May 19, 2017
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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 <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 Vrije Universiteit Brussel, Belgium
- Paris Koutris University of Wisconsin-Madison, USA
- Jacek Sroka University of Warsaw, Poland
- Jeffrey Ullman Stanford University
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Program Committee
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- Paris Koutris, University of Wisconsin-Madison (CHAIR)
- Foto Afrati, National Technical University of Athens
- Sourav S. Bhowmick, Nanyang Technological University
- Yingyi Bu, Couchbase
- Ahmed Eldawy, University of California, Riverside
- Todd Green, LogicBlox
- Jan Hidders, Vrije Universiteit Brussel
- Asterios Katsifodimos, Technical University of Berlin
- Paraschos Koutris, University of Wisconsin-Madison
- Nectarios Koziris, National Technical University of Athens
- Ulf Leser, Humboldt-Universität zu Berlin
- Dionysios Logothetis, Facebook
- Frank McSherry
- Frank Neven, Hasselt University
- Daniel de Oliveira, Fluminense Federal University
- Krzysztof Onak, IBM T.J. Watson Research Center
- Fabio Porto, National Laboratory of Scientific Computation
- Chris Re, Stanford University
- Krzysztof Rzadca, University of Warsaw
- Semih Salihoglu, University of Waterloo
- Mark Santcroos, Rutgers University
- Francesco Silvestri, IT University of Copenhagen
- Yogesh Simmhan, Indian Institute of Science, Bangalore
- Jacek Sroka, University of Warsaw
- Dan Suciu, University of Washington
- Jeffrey Ullman, Stanford University
- Theodore Vassilakis, Microsoft
- Jianwu Wang, University of Maryland, Baltimore County
- Zhengkui Wang, National University of Singapore
- Ke Yi, Hong Kong University of Science and Technology
- Eiko Yoneki, University of Cambridge
- Matei Zaharia, Stanford University
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