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 in cloud computing environment. 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 Cloud

Submit your paper(s) in PDF file at the BigDataMR2012 submission site: https://www.easychair.org/conferences/?conf=bigdatamr2012. Papers should be limited up to 8 pages in IEEE CS format. The template files for LATEX or WORD 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 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 and International Journal of High Performance Computing Applications.

Geoffrey Charles Fox, Indiana University, USA

Xian-He Sun, Illinois Institute of Technology, USA

Jian Pei, Simon Fraser University, Canada

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

Program Committee

Aleksandar Stupar, Saarland University, Germany
Beth Plale, Indiana University, USA
Dong Yuan, Swinburne University of Technology, Australia
Elke A. Rundensteiner, Worcester Polytechnic Institute, USA
Gabriel Antoniu, INRIA Rennes - Bretagne, Atlantique/IRISA, France
Gunter Saake, University of Magdeburg, Germany
Haifeng Wang, Baidu
Huafei Zhu, I2R, A*STAR, Singapore
Huan Liu, Accenture Technology Labs
Jerome Rolia, HP Labs
Joao Gama, University Porto, Portugal
Liana Fong, IBM Research
Liangliang Cao, IBM
Lianyong Qi, Qufu Normal University, China
Lixin Gao, University of Massachusetts Amherst, USA
Maozhen Li, Brunel University, UK
Melek Önen, EURECOM, France
Roberto Di Pietro, Roma Tre University of Rome, Italy
Roman Y. Shtykh, Waseda University, Japan
Shipin Chen, CSIRO, Australia
Steven C.H. Hoi, Nanyang Technological University, Singapore
Ting Yu, North Carolina State University, USA
Wanchun Dou, Nanjing University, China
Xingquan Zhu, University of Technology Sydney, Australia
Yihua Huang, Nanjing University, China
Yun Yang, Swinburne University of Technology, Australia
Zhihua Zhou, Nanjing University, China