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5. Parallel and Distributed Data Management and Analytics

Many areas of science, industry, and commerce are producing extreme-scale data that must be processed—stored, managed, analyzed—in order to extract useful knowledge. This topic seeks papers in all aspects of distributed and parallel data management and data analysis. For example, HPC in situ data analytics, cloud and grid data-intensive processing, parallel storage systems, and scalable data processing workflows are all in the scope of this topic.


  • Parallel, replicated, and highly-available distributed databases
  • Data-intensive clouds and grids
  • HPC scientific data analytics
  • Middleware for processing large-scale data
  • Programming models for parallel and distributed data analytics
  • Workflow management for data analytics
  • Coupling HPC simulations with in situ data analysis
  • Parallel data visualization
  • Distributed and parallel transaction and query processing and information retrieval
  • Internet-scale data-intensive applications
  • Sensor network data management
  • Cloud and HPC storage architectures and systems
  • Parallel data streaming and data stream mining
  • Parallel and distributed knowledge discovery and data mining
  • New storage hierarchies in distributed data systems based on NVRAM technologies


Chair: Tom Peterka (Argonne National Laboratory, USA)
Local chair: Bruno Raffin (INRIA, France)

Christopher Carothers (Rensselaer Polytechnic Institute, USA)
Toni Cortes (BSC, Spain)
Matthieu Dorier (Argonne National Laboratory, USA)
Wolfgang Frings (JSC, Germany)
Patrick Martin (Queen’s University, Kingston, Canada)
Yang-Sae Moon (Kangwon National University, Korea)

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