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[protege-discussion] Extended CFP - BigDataMR2012 (International Symposium on Big Data and MapReduce), 1-3 Nov. 2012, Xiangtan, China

Sean Zhang xuyun.zhangcs at
Tue Jun 26 04:53:12 PDT 2012

To allow for most deadline extension requests, we have extended the
submission deadline to 10 July 2012. But this will be firm.

Call for papers: 2012 International symposium on Big Data and MapReduce
(BigDataMR2012), 1-3 Nov. 2012, Xiangtan, China. The website is

Key dates:
Deadline for Paper Submission:               Extended to July 10, 2012
Notification of Acceptance:                       July 30, 2012
Camera Ready Copies:                            August 10, 2012

Submission site and requirements: 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.

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).

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!
 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.

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:

·      Big Data theory, applications and challenges
·      Recent development in Big Data and MapReduce
·      Big Data mining and analytics
·      Big Data visualization
·      Large data stream processing on cloud
·      Large incremental datasets on cloud
·      Distributed and federated datasets
·      NoSQL data stores and DB scalability
·      Big Data sharing and privacy preserving on cloud
·      Security, trust and risk in Big Data
·      Big Data placement, scheduling, and optimization
·      Extension of the MapReduce programming model
·      Distributed file systems for Big Data
·      MapReduce for Big Data processing
·      MapReduce on hybrid cloud
·      MapReduce on heterogeneous distributed environments
·      Performance characterization, evaluation and optimization
·      Simulation and debugging of MapReduce and Big Data systems and tools
·      Security, privacy, reliability, trust and privacy in MapReduce
·      Volume, Velocity and Variety of Big Data on Cloud
·      Multiple source data processing and integration with MapReduce
·      Resource scheduling and SLA for MapReduce
·      Big Data processing tools based on MapReduce
·      Storage and computation management of Big Data
·      Large-scale scientific workflow in support of Big Data processing on

General Chairs:
Geoffrey Charles Fox, Indiana University, USA
Xian-He Sun, Illinois Institute of Technology, USA
Jian Pei, Simon Fraser University, Canada

Program Chairs:
Xuyun Zhang, University of Technology Sydney, Australia
Suraj Pandey, IBM
Xiaolin Li, University of Florida, USA
Jinjun Chen, University of Technology Sydney, Australia

Any enqueries, please direct to Xuyun Zhang at xyzhanggz at

PhD Student Xuyun(Sean) Zhang
Faculty of Engineering and Information Technology,
University of Technology, Sydney,
PO Box 123, Broadway NSW 2007, Australia.

 Email: xyzhanggz at, or Xuyun.Zhang at
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