Topic 4: Data Management, Analytics and Machine Learning

Global Chair: Ruggero G. Pensa, University of Turin

Local Chair: Nikos Ntarmos, Huawei Technologies R&D UK

Description

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, cloud and grid data-intensive processing, parallel and distributed machine learning, HPC in situ data analytics, parallel storage systems, scalable data processing workflows, federated learning and distributed stream processing are all in the scope of this topic.

Focus

  • Parallel, replicated, and highly-available distributed databases
  • Cloud and HPC storage architectures and systems
  • Scientific data analytics (Big Data or HPC based approaches)
  • 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, query processing and information retrieval
  • Internet-scale data-intensive applications
  • Sensor network data management
  • Data-intensive clouds and grids
  • Parallel data streaming and data stream mining
  • Federated learning
  • New storage hierarchies in distributed data systems
  • Parallel and distributed machine learning, knowledge discovery and data mining
  • Privacy and trust in parallel and distributed data management and analytics systems
  • IoT data management and analytics
Euro-Par
University of Glasgow

Gold Sponsors

Red Hat
sicsa

Silver Sponsors

Xilinx