Call for papers: 2012 International symposium on Big Data and MapReduce
(BigDataMR2012), 1-3 Nov. 2012, Xiangtan, China. The website is <a href="http://www.swinflow.org/confs/bigdatamr2012/" target="_blank">http://www.swinflow.org/confs/bigdatamr2012/</a>.<br>
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Key dates:<br>
Deadline for Paper Submission: July 10, 2012 (firm)<br>
Notification of Acceptance: July 30, 2012<br>
Camera Ready Copies: August 10, 2012<br>
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Submission site and requirements:<br>
<a href="https://www.easychair.org/conferences/?conf=bigdatamr2012" target="_blank">https://www.easychair.org/conferences/?conf=bigdatamr2012</a>.
Submit your paper(s) in PDF file. Papers should be limited up to 8
pages in IEEE CS format. The template files for LATEX or WORD can be
downloaded from the symposium website. All papers will be peer reviewed
by two or three pc members. Submitting a paper to the workshop means
that if the paper is accepted, at least one author should register to
CGC2012 and attend the conference to present the paper.<br>
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<br>
Publications:<br>
All accepted papers will appear in the proceedings published by IEEE
Computer Society (EI indexed). Selected papers will be invited to
special issues of CGC2012 in Concurrency and Computation: Practice and
Experience, Future Generation Computer Systems, International Journal
of High Performance Computing Applications and Computing (Springer).<br>
<br>
Introduction:<br>
Big data is an emerging paradigm applied to datasets whose size is
beyond the ability of commonly used software tools to capture, manage,
and process the data within a tolerable elapsed time. Such datasets are
often from various sources (Variety) yet unstructured such as social
media, sensors, scientific applications, surveillance, video and image
archives, Internet texts and documents, Internet search indexing,
medical records, business transactions and web logs;and are of large
size (Volume) with fast data in/out (Velocity). Various technologies are
being discussed to support the handling of big data such as massively
parallel processing databases, scalable storage systems, cloud computing
platforms, and MapReduce. MapReduce is a distributed programming
paradigm and an associated implementation to support distributed
computing over large datasets on cloud. This symposium aims at providing
a forum for researchers, practitioners and developers from different
background areas!<br>
such as cloud computing, distributed computing and database area to
exchange the latest experience, research ideas and synergic research and
development on fundamental issues and applications about big data and
MapReduce in cloud environments. The symposium solicits high quality
research results in all related areas.<br>
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Topics:<br>
The objective of the symposium is to invite authors to submit original
manuscripts that demonstrate and explore current advances in all aspects
of big data and MapReduce. The symposium solicits novel papers on a
broad range of topics, including but not limited to:<br>
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· Big Data theory, applications and challenges<br>
· Recent development in Big Data and MapReduce<br>
· Big Data mining and analytics<br>
· Big Data visualization<br>
· Large data stream processing on cloud<br>
· Large incremental datasets on cloud<br>
· Distributed and federated datasets<br>
· NoSQL data stores and DB scalability<br>
· Big Data sharing and privacy preserving on cloud<br>
· Security, trust and risk in Big Data<br>
· Big Data placement, scheduling, and optimization<br>
· Extension of the MapReduce programming model<br>
· Distributed file systems for Big Data<br>
· MapReduce for Big Data processing<br>
· MapReduce on hybrid cloud<br>
· MapReduce on heterogeneous distributed environments<br>
· Performance characterization, evaluation and optimization<br>
· Simulation and debugging of MapReduce and Big Data systems and tools<br>
· Security, privacy, reliability, trust and privacy in MapReduce<br>
· Volume, Velocity and Variety of Big Data on Cloud<br>
· Multiple source data processing and integration with MapReduce<br>
· Resource scheduling and SLA for MapReduce<br>
· Big Data processing tools based on MapReduce<br>
· Storage and computation management of Big Data<br>
· Large-scale scientific workflow in support of Big Data processing on Cloud<br>
<br>
General Chairs:<br>
Geoffrey Charles Fox, Indiana University, USA<br>
Xian-He Sun, Illinois Institute of Technology, USA<br>
Jian Pei, Simon Fraser University, Canada<br>
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Program Chairs:<br>
Xuyun Zhang, University of Technology Sydney, Australia<br>
Suraj Pandey, IBM<br>
Xiaolin Li, University of Florida, USA<br>
Jinjun Chen, University of Technology Sydney, Australia<br>
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Any enqueries, please direct to Xuyun Zhang at <a href="mailto:xyzhanggz@gmail.com" target="_blank">xyzhanggz@gmail.com</a><br clear="all"><br>-- <br><div><font face="courier new,monospace"><span style="font-family:courier new,monospace">*************************************************</span><br style="font-family:courier new,monospace">
</font><span style="font-family:courier new,monospace"><font face="courier new,monospace">PhD Student Xuyun(Sean) Zhang<br></font></span></div><div><div><font face="courier new,monospace"><span style="font-family:courier new,monospace">Faculty of Engineering and Information Technology,<br>
University of Technology, Sydney,<br>PO Box 123, Broadway NSW 2007, </span><span style="font-family:courier new,monospace">Australia.</span> </font></div><font face="courier new,monospace">
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</font></span></div>
<font face="courier new,monospace">
</font></div><div><span style="font-family:courier new,monospace"><font face="courier new,monospace">Email: <a href="mailto:xyzhanggz@gmail.com" target="_blank">xyzhanggz@gmail.com</a>, or <a href="mailto:Xuyun.Zhang@student.uts.edu.au" target="_blank">Xuyun.Zhang@student.uts.edu.au</a><br>
</font></span></div>
<font face="courier new,monospace">
</font><div><span style="font-family:courier new,monospace"><font face="courier new,monospace">URL: <a href="https://sites.google.com/site/seanzhangcs/" target="_blank">https://sites.google.com/site/seanzhangcs/</a></font></span></div>
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</font><div><span style="font-family:courier new,monospace"></span><span style="font-family:courier new,monospace"><font face="courier new,monospace">*************************************************</font></span></div><br>