4th International Workshop on Big Data Analytics: Challenges and Opportunities (BDAC-13)
In Cooperation with ACM/IEEE SC13, 17 November 2013, Denver, CO, USA.
<http://www.ornl.gov/sci/
Call For Papers
Important Deadlines
Paper Submission: September 15, 2013
Acceptance Notice: October 15, 2013
Camera-Read Copy: November 25, 2013
The 4th International Workshop on Big Data Analytics: Challenges, and Opportunities (BDAC-13), to be held in cooperation with 24th IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC13), 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 mus
t 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 modern 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 in climate change, remote sensing, biology, healthcare, fusion, combustion, materials, astrophysics, web, and social networks
* Future research challenges for big data analytics
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 BDAC-13 (http://www.ornl.gov/sci/
Organizing Committee:
Ranga Raju Vatsavai, Oak Ridge National Laboratory, USA
Scott A Klasky, Oak Ridge National Laboratory, USA
Manish Parashar, Rutgers University, USA
In Cooperation with ACM/IEEE SC13, 17 November 2013, Denver, CO, USA.
<http://www.ornl.gov/sci/
Call For Papers
Important Deadlines
Paper Submission: September 15, 2013
Acceptance Notice: October 15, 2013
Camera-Read Copy: November 25, 2013
The 4th International Workshop on Big Data Analytics: Challenges, and Opportunities (BDAC-13), to be held in cooperation with 24th IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC13), 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 mus
t 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 modern 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 in climate change, remote sensing, biology, healthcare, fusion, combustion, materials, astrophysics, web, and social networks
* Future research challenges for big data analytics
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 BDAC-13 (http://www.ornl.gov/sci/
Organizing Committee:
Ranga Raju Vatsavai, Oak Ridge National Laboratory, USA
Scott A Klasky, Oak Ridge National Laboratory, USA
Manish Parashar, Rutgers University, USA
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