<div dir="ltr"><p class=""><span style="background-repeat:initial initial">[We apologize if you
receive multiple copies of this CFP.]</span></p>
<p class=""> </p>
<p class="">Call for Papers – Deadline July 30, 2014<br>
<br>
</p>
<p class="">The 3rd
International Symposium on MapReduce and Big Data Infrastructure (MR.BDI 2014)</p>
<p class="">03-05 December 2014, Sydney, Australia</p>
<p class="">Co-located
with the 4th IEEE <span lang="EN-US">International Conference on </span>Big Data and Cloud Computing <span lang="EN-US">(</span><a href="http://www.swinflow.org/confs/bdcloud2014/"><span style="color:windowtext">BdCloud 2014</span></a>). Sponsored by <span lang="EN-US">Sponsored by IEEE </span><a href="http://www.ieeetcsc.org/"><span lang="EN-US" style="color:windowtext">TCSC</span></a><span lang="EN-US"> Technical
Area on Big Data and MapReduce</span></p>
<table class="" border="0" cellspacing="0" cellpadding="0">
<tbody><tr>
<td style="padding:0.75pt">
<p class="MsoNormal"><a href="http://www.swinflow.org/confs/mrbdi2014/">http://www.swinflow.org/confs/mrbdi2014/</a></p>
<p class="MsoNormal" style="background-repeat:initial initial">--------------------------------------------------------------------------------------------------------------------------------</p>
<p class="MsoNormal" style="background-repeat:initial initial">The emergence of big data and the potential to undertake complex
analysis of very large data sets is, essentially, a consequence of
recent advances in the technology that allow this. The development of
cloud computing over the last few years represents the single most
important contributor to the big data trend, with cloud infrastructure such
as compute, storage and analytical tools and apps now widely available.
The convergence of big data and cloud computing are having far reaching
implications that indeed are changing the world. MapReduce, a widely-adopted parallel and distributed programming
paradigm for processing large-scale data sets, becomes much more powerful,
scalable, elastic and cost-effective when integrated in cloud systems as it
can benefits from the salient characteristics of cloud computing. Based on
the MapReduce paradigm and other relevant techniques like HDFS, a series of
applications and higher level platforms such as Hadoop, Hive, Twister, Spark,
Pregel, to name a few, have been proposed and developed. MapReduce and the
emerging tools in cloud are ideal for enterprises with large data centres and
scientific communities to address the challenges posed by big data
applications. The MapReduce paradigm itself, emerging MapReduce based big
data tools and applications, and big data infrastructure such as cloud
systems are evolving fast, and therefore need extensive investigations
from various research communities.</p>
<p class="MsoNormal" style="background-repeat:initial initial">This symposium aims at providing a forum for researchers,
practitioners and developers from different background areas such as cloud
computing, distributed computing, large-scale data management and database
areas to exchange the latest experience, research ideas and synergic
research and development on fundamental issues and applications about
MapReduce, MapReduce based platforms and emerging big data infrastructure.
The symposium solicits high quality research results in all related areas.</p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">This is the third instalment of the symposium,
following the successful events of</span> <a href="http://www.swinflow.org/confs/mrbdi2013/"><span style="color:windowtext">2013</span></a> <span style="background-repeat:initial initial">(Australia)
and</span> <a href="http://www.swinflow.org/confs/bigdatamr2012"><span style="color:windowtext">2012</span></a> <span style="background-repeat:initial initial">(China).</span></p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">Topics: </span></p>
<p class="">The objective
of the symposium is to invite authors to submit original manuscripts that
demonstrate and explore current advances in all aspects of MapReduce and big data infrastructure. The symposium
solicits novel papers on a broad range of topics, including but not limited
to:</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Challenges and
Opportunities in <span lang="EN-US">MapReduce</span> based Big Data Tools and Applications</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span><span lang="EN-US">Recent Development in MapReduce</span> and Big Data Infrastructure</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Developing,
Debugging and Testing Issues of MapReduce based Big Data
Tools </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Performance Tuning and Optimization for
MapReduce and Big Data Infrastructure </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Benchmarking, Evaluation, Simulation
for MapReduce based Big Data Tools </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Iterative
/ Recursive MapReduce Systems</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Computational
Theory for MapReduce based Systems</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span><span lang="EN-US">Extension of the MapReduce
Programming </span>Paradigm</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span><span lang="EN-US">Distributed File Systems for </span>MapReduce and Emerging Big Data Tools</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Algorithm
Analysis and Design with MapReduce Paradigm</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Resource
Scheduling and SLA of MapReduce for Multiple Users</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Heterogeneity
and Fault-tolerance in MapReduce based Systems and Big Data
Infrastructure</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Privacy, <span lang="EN-US">Security, </span>T<span lang="EN-US">rust and Risk in </span>MapReduce and Big Data
Infrastructure </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Integration
of MapReduce and Emerging Big Data Tools with Cloud / Grid
Systems </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>MapReduce in Hybrid /
Fabricated / Federated Cloud Systems </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Social
Networks Analyses with MapReduce</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Data <span lang="EN-US">Mining</span>, <span lang="EN-US">Analytics</span>, and Visualization using
MapReduce </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Big Stream /
Incremental Data Processing using MapReduce</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Big
Scientific, Genomic and Healthcare Data Processing with MapReduce</p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span>Industrial
Experience and Use Cases of MapReduce based Applications </p>
<p class="" style="margin-left:36pt"><span style="font-family:Symbol">·<span style="font-size:7pt;font-family:'Times New Roman'">
</span></span><span lang="EN-US">Recent Development</span> Open Source Big Data
Infrastructure </p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="background-repeat:initial initial">Submission Guidelines:</p>
<p class="MsoNormal" style="background-repeat:initial initial">Submit your paper(s) in PDF file at
the MR.BDI2014 submission site: <a href="https://www.easychair.org/conferences/?conf=mrbdi2014"><span style="color:windowtext">https://www.easychair.org/conferences/?conf=mrbdi2014</span></a>. Papers should be limited up to 8 pages in IEEE
CS format. The template files for <a href="http://kpnm.hnust.cn/confs/cgc2012/IEEECS_CPS_LaTeX_Letter_2Col.zip" title="Template files for LATEX."><span style="color:windowtext">LATEX</span></a> or <a href="http://kpnm.hnust.cn/confs/cgc2012/IEEECS_CPS_8.5x11x2.zip" title="Template files for WORD."><span style="color:windowtext">WORD</span></a>can
be downloaded here. All papers will be peer reviewed by two or three pc
members. Submitting a paper to the symposium means that if the paper is
accepted, at least one author should register to <a href="http://www.swinflow.org/confs/bdcloud2014/"><span style="color:windowtext">BdCloud
2014</span></a> and attend the conference to present the paper.</p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="background-repeat:initial initial">Publication of paper:</p>
<p class="MsoNormal" style="background-repeat:initial initial">All accepted papers will appear in the proceedings published by IEEE
Computer Society (EI indexed). Distinguished papers will be invited to
special issues of BdCloud2014 in Concurrency and Computation:
Practice and Experience, Journal of Network and Computer Applications,
Journal of Computer and System Sciences, and IEEE Transactions on Cloud
Computing.</p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="background-repeat:initial initial">Important Dates:</p>
<p class="">Deadline for Paper
Submission: July
30, 2014</p>
<p class="">Notification
of Acceptance: September
25, 2014</p>
<p class="">Camera Ready Copies: October 15, 2014</p>
<p class="">Registration
Due: October 15, 2014</p>
</td>
</tr>
</tbody></table>
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<p class="MsoNormal"><span style="color:black">General
Chairs:</span></p>
<p class="">Yanpei Chen,
Cloudera, USA</p>
<p class="">Rajkumar Buyya, University
of Melbourne, Australia</p>
<p class="">Jinjun Chen,
University of Technology, Sydney, Australia</p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="text-align:justify"><span style="color:black">Program Committee Chairs:</span></p>
<p class="">Nazanin Borhan, University
of Technology Sydney, Australia</p>
<p class="">Xuyun Zhang,
University of Technology Sydney, Australia</p>
<p class="">Suraj Pandey, IBM
Australia Research Lab, Australia</p>
<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">------------------------------------------------------------------------------------------------------------------------------</span></p>
<p class="MsoNormal" style="text-align:justify"><span style="color:black">Program Committees:</span></p>
<p class="">Gunter Saake,
University of Magdeburg, Germany</p>
<p class="">Andreas Thor,
University of Leipzig, Germany</p>
<p class="">Javid Taheri,
University of Sydney, Australia</p>
<p class="">Amund Tveit,
Memkite, Norway</p>
<p class="">Soudip Roy
Chowdhury, INRIA, Saclay, France</p>
<p class="">Bahman Javadi,
University of Western Sydney, Australia</p>
<p class="">Paolo Trunfio,
University of Calabria, Italy</p>
<p class="">Chi Yang,
University of Technology Sydney, Australia</p>
<p class="">Liana Fong, IBM
Research, USA</p>
<p class="">Nikzad Babaii
Rizvandi, University of Sydney, Australia</p>
<p class="">Shipin Chen,
CSIRO, Australia</p>
<p class="">Roberto Di
Pietro, Roma Tre University of Rome, Italy</p>
<p class="">Jun-Ki Min,
Korea university of technology, South Korea</p>
<p class="">Ray C.C. Cheung,
City University of Hong Kong, Hong Kong</p>
<p class="">Hadi Mashinchi,
Simavita, Australia</p>
<p class="">Chao Wang,
University of Science and Technology of China, China</p>
<p class="">Hidemoto Nakada,
AIST, Japan</p>
<p class="">Boyu Zhang,
University of Delaware, USA</p>
<p class="MsoNormal"> </p></div>