<div dir="ltr"><p class="MsoNormal"><span style="font-size:7.5pt;font-family:Arial,sans-serif">[We apologize
if you receive multiple copies of this CFP.]</span></p>
<p class="MsoNormal"><br></p><p class="MsoNormal">Call
for Papers – Deadline July 30, 2014<br>
<br></p><p class="MsoNormal">The 3rd
International Symposium on MapReduce and Big Data Infrastructure (MR.BDI 2014)</p>
<p class="MsoNormal" style="background-repeat:initial initial">03-05 December 2014, Sydney, Australia</p>
<p class="MsoNormal" style="background-repeat:initial initial">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 <a href="http://www.ieeetcsc.org/"><span style="color:windowtext">TCSC</span></a> Technical
Area on Big Data and MapReduce</span></p>
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<p class="MsoNormal"><a href="http://www.swinflow.org/confs/mrbdi2014/">http://www.swinflow.org/confs/mrbdi2014/</a></p>
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<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> <span style="background-repeat:initial initial"><a href="http://www.swinflow.org/confs/mrbdi2013/"><span style="color:windowtext">2013</span></a></span> <span style="background-repeat:initial initial">(Australia) and</span> <span style="background-repeat:initial initial"><a href="http://www.swinflow.org/confs/bigdatamr2012"><span style="color:windowtext">2012</span></a></span> <span style="background-repeat:initial initial">(China).</span></p>
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<p class="MsoNormal" style="background-repeat:initial initial"><span style="background-repeat:initial initial">Topics: </span></p>
<p class="MsoNormal" style="background-repeat:initial initial">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="MsoNormal" style="margin-left:18pt">· Challenges and
Opportunities in <span lang="EN-US">MapReduce</span> based Big Data Tools and Applications</p>
<p class="MsoNormal" style="margin-left:18pt">· <span lang="EN-US">Recent
Development in MapReduce</span> and
Big Data Infrastructure</p>
<p class="MsoNormal" style="margin-left:18pt">· Developing, Debugging and
Testing Issues of MapReduce based Big Data Tools </p>
<p class="MsoNormal" style="margin-left:18pt">· Performance Tuning and Optimization for
MapReduce and Big Data Infrastructure </p>
<p class="MsoNormal" style="margin-left:18pt">· Benchmarking, Evaluation, Simulation
for MapReduce based Big Data Tools </p>
<p class="MsoNormal" style="margin-left:18pt">· Iterative / Recursive MapReduce
Systems</p>
<p class="MsoNormal" style="margin-left:18pt">· Computational
Theory for MapReduce based Systems</p>
<p class="MsoNormal" style="margin-left:18pt">· <span lang="EN-US">Extension
of the MapReduce Programming </span>Paradigm</p>
<p class="MsoNormal" style="margin-left:18pt">· <span lang="EN-US">Distributed
File Systems for </span>MapReduce
and Emerging Big Data Tools</p>
<p class="MsoNormal" style="margin-left:18pt">· Algorithm Analysis and Design with
MapReduce Paradigm</p>
<p class="MsoNormal" style="margin-left:18pt">· Resource
Scheduling and SLA of MapReduce for Multiple Users</p>
<p class="MsoNormal" style="margin-left:18pt">· Heterogeneity and
Fault-tolerance in MapReduce based Systems and Big Data Infrastructure</p>
<p class="MsoNormal" style="margin-left:18pt">· 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="MsoNormal" style="margin-left:18pt">· Integration of MapReduce and
Emerging Big Data Tools with Cloud / Grid Systems </p>
<p class="MsoNormal" style="margin-left:18pt">· MapReduce in Hybrid /
Fabricated / Federated Cloud Systems </p>
<p class="MsoNormal" style="margin-left:18pt">· Social Networks Analyses with
MapReduce</p>
<p class="MsoNormal" style="margin-left:18pt">· Data <span lang="EN-US">Mining</span>, <span lang="EN-US">Analytics</span>, and Visualization using MapReduce </p>
<p class="MsoNormal" style="margin-left:18pt">· Big Stream / Incremental
Data Processing using MapReduce</p>
<p class="MsoNormal" style="margin-left:18pt">· Big Scientific, Genomic and
Healthcare Data Processing with MapReduce</p>
<p class="MsoNormal" style="margin-left:18pt">· Industrial Experience and Use Cases
of MapReduce based Applications </p>
<p class="MsoNormal" style="margin-left:18pt">· <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>
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<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="MsoNormal" style="background-repeat:initial initial">Deadline for Paper
Submission:
July 30, 2014</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt">Notification of
Acceptance: September
25, 2014</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt">Camera Ready
Copies: October
15, 2014</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt">Registration Due: October 15, 2014</p>
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