The 3rd International Workshop on Big Data Analytics: Challenges, and Opportunities (BDAC-13), to be held in cooperation with 25th 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 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 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). Submission instructions will be posted here soon.
Proceedings: All accepted papers will be included in the SC companion proceedings to be published by IEEE digital libary.