[Storage-research-list] CFP: [MR.BDI 2014] The 3rd International Symposium on MapReduce and Big Data Infrastructure, 03-05 December 2014, Sydney, Australia

[Nazanin Borhan]

[We apologize if you receive multiple copies of this CFP.]



Call for Papers – Deadline July 30, 2014

 The 3rd International Symposium on MapReduce and Big Data Infrastructure
(MR.BDI 2014)

03-05 December 2014, Sydney, Australia

Co-located with the 4th IEEE International Conference on Big Data and Cloud
Computing (BdCloud 2014 <http://www.swinflow.org/confs/bdcloud2014/>).
Sponsored by Sponsored by IEEE TCSC <http://www.ieeetcsc.org/> Technical
Area on Big Data and MapReduce

http://www.swinflow.org/confs/mrbdi2014/

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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.

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.

This is the third instalment of the symposium, following the successful
events of 2013 <http://www.swinflow.org/confs/mrbdi2013/> (Australia) and
2012 <http://www.swinflow.org/confs/bigdatamr2012> (China).

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Topics:

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:

·      Challenges and Opportunities in MapReduce based Big Data Tools and
Applications

·      Recent Development in MapReduce and Big Data Infrastructure

·   Developing, Debugging and Testing Issues of MapReduce based Big Data
Tools

·      Performance Tuning and Optimization for MapReduce and Big Data
Infrastructure

·   Benchmarking, Evaluation, Simulation for MapReduce based Big Data
Tools

·   Iterative / Recursive MapReduce Systems

·      Computational Theory for MapReduce based Systems

·      Extension of the MapReduce Programming Paradigm

·      Distributed File Systems for MapReduce and Emerging Big Data Tools

·   Algorithm Analysis and Design with MapReduce Paradigm

·      Resource Scheduling and SLA of MapReduce for Multiple Users

·   Heterogeneity and Fault-tolerance in MapReduce based Systems and Big
Data Infrastructure

·      Privacy, Security, Trust and Risk in MapReduce and Big Data
Infrastructure

·     Integration of MapReduce and Emerging Big Data Tools with Cloud /
Grid Systems

·      MapReduce in Hybrid / Fabricated / Federated Cloud Systems

·   Social Networks Analyses with MapReduce

·   Data Mining, Analytics, and Visualization using MapReduce

·   Big Stream / Incremental Data Processing using MapReduce

·   Big Scientific, Genomic and Healthcare Data Processing with MapReduce

·   Industrial Experience and Use Cases of MapReduce based Applications

·   Recent Development Open Source Big Data Infrastructure

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Submission Guidelines:

Submit your paper(s) in PDF file at the MR.BDI2014 submission site:
https://www.easychair.org/conferences/?conf=mrbdi2014. Papers should be
limited up to 8 pages in IEEE CS format. The template files for
LATEX<http://kpnm.hnust.cn/confs/cgc2012/IEEECS_CPS_LaTeX_Letter_2Col.zip>
 or WORD <http://kpnm.hnust.cn/confs/cgc2012/IEEECS_CPS_8.5x11x2.zip>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 BdCloud
2014<http://www.swinflow.org/confs/bdcloud2014/> and
attend the conference to present the paper.

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Publication of paper:

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.

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Important Dates:

Deadline for Paper Submission:                July 30, 2014

Notification of Acceptance:                      September 25, 2014

Camera Ready Copies:                            October 15, 2014

Registration Due:                                   October 15, 2014
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