[ecoop-info] CFP: 3rd International Workshop on Petascale Data Analytics: Challenges and Opportunities (PDAC-12)

Ivan Rodero irodero at cac.rutgers.edu
Tue Sep 18 15:59:01 CEST 2012

[Apologize for multiple/cross-postings]

3rd International Workshop on Petascale Data Analytics: Challenges and Opportunities (PDAC-12)
(aka Big Data Analytics)

In Cooperation with ACM/IEEE SC12, 12 November 2012, Salt Lake City, Utah, USA. 

Call For Papers

Important Deadlines
Paper Submission 	
September 17, 2012
September 30, 2012
Acceptance Notice 	
October 15, 2012
Camera-Read Copy	
December 1, 2012
The 2nd International Workshop on Petascale Data Analytics: Challenges, and Opportunities (PDAC-12), to be held in cooperation with 24th IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), provides an international platform to share and discuss recent research results in adopting high-end computing including clouds and distributed computing resources for petascale - exascale data frameworks, analytics, and visualization.

Synopsis: In the last ten years, computing capability has increased many-fold, and correspondingly data volumes have grown by an even larger amount. Many traditional application domains have now become data intensive. It is estimated that organizations with high-performance computing infrastructures and data centers are doubling the amount of data that they are archiving every year. Recent advances in computing architectures require that middleware and application software be reengineered to fully exploit heterogeneous resources, memory hierarchies, and I/O pipelines. Cloud computing has become a practical and cost effective solution for providers and consumers, ranging from business analytics to scientific computing. The utility of cloud computing has been shown to provide significant benefits in data mining, machine learning and knowledge discovery. Cloud computing also has great potential to revolutionize extreme scale data analytics; but there are many obstacles which must be overcome to gain wide spread adoption. The integration of HPC and cloud infrastructure, for example, must be addressed in a manner that is both usable and scalable. This workshop intends to bring together members of academia, government and industry to discuss new and emerging trends in computing architectures, programming models, I/O services, and data analytics. This workshop will also identify the greatest challenges in embracing high-end computing infrastructure for scaling I/O and algorithms to extreme scale datasets. We invite researchers, developers, and users to participate in this workshop to share, contribute, and discuss the emerging challenges in developing knowledge discovery solutions and frameworks targeting clouds and high-end computing platforms.

Topics: The major topics of interest to the workshop include but are not limited to:

Programing models and tools needed for data mining (DM), machine learning (ML), and knowledge discovery (KD)
Fault tolerant data mining in clouds
Storing and mining the streaming data in clouds
Programming models for the integration of HPC and cloud technologies
I/O pipelines
Techniques for visualizing massive datasets
Visualization in virtualized environments
Storage technologies for clouds
Data movement and caching
Distributed file systems
Scalability and complexity issues
Security and privacy issues
Algorithms that best suit cloud and distributed computing platforms
Performance studies comparing various distributed file systems for data intensive applications
Performance comparisons between clouds and HPC systems
Workflow technologies for cloud computing
Customizations and extensions of existing software infrastructures such as Hadoop and Dryad for extreme scale data analytics
Applications and case studies
Future research challenges for petascale data analytics and beyond
Paper Submission: This is an open call-for-papers. We invite regular research paper submissions (maximum 10 pages), work-in-progress (5 pages), demo papers (3 pages), and position papers (3 pages). For detailed submission instructions and paper templates, consult PDAC-12 (http://www.ornl.gov/sci/knowledgediscovery/CloudComputing/PDAC-SC12/) website. All accepted papers will be included in the workshop proceedings to be published by IEEE digital libary.

Organizing Committee:

Program Chairs
Ranga Raju Vatsavai, Oak Ridge National Laboratory, USA 
Scott A Klasky, Oak Ridge National Laboratory, USA
Manish Parashar, Rutgers University, USA

Publicity Chairs
Varun Chandola, Oak Ridge National Laboratory, USA

Program Committee (under construction)
Mohammad H. Abbasi, Oak Ridge National Laboratories, USA 
Gagan Agrawal, Ohio State University, USA 
Kanishka Bhaduri, NASA, USA 
Joydeep Ghosh, UT-Austin, USA 
Amol Ghoting, IBM T. J. Watson Research 
Daniel S. Katz, University of Chicago, USA 
Kun Liu, Yahoo! Labs, USA 
Yan Liu, IBM TJ Watson, USA 
Qing Liu, Oak Ridge National Laboratory, USA 
Gerald F. Lofstead, Sandia National Laboratories, USA 
Kenneth Moreland, Sandia National Laboratories, USA
Norbert Podhorszki, Oak Ridge National Laboratories, USA 
Sanjay Ranka, University of Florida, USA 
Joel H. Saltz, Emory University, US 
Karsten Schwan, Georgia Institute of Technology, USA 
Kesheng (John) Wu, Lawrence Berkeley National Laboratory,  USA

Ivan Rodero, Ph.D.
Rutgers Discovery Informatics Institute (RDI2)                                                         
NSF Center for Cloud and Autonomic Computing (CAC)
Department of Electrical and Computer Engineering            
Rutgers, The State University of New Jersey   
Office: CoRE Bldg, Rm 625                                                       
94 Brett Road, Piscataway, NJ 08854-8058
Phone: (848) 228-6474
Fax:   (732) 445-0593
Email: irodero at rutgers dot edu
WWW: http://nsfcac.rutgers.edu/people/irodero

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