Topic 5: Parallel and Distributed Data Management

Description

The exponential information growth transforms many compute challenges into data processing challenges and therefore data intensive applications are heavily studied both in the context of HPC and cloud environments. Data intensive applications require new approaches and efficient techniques to store, manage, and process the locally stored or geographically dispersed data to cope with this data explosion.
Despite recent advances in application development and the design of the underlying storage systems, it is still necessary to improve the provisioning, staging, manipulation, continuous maintenance and monitoring of data hosted in autonomous, distributed and heterogeneous systems.
The parallel and concurrent execution at all levels remains key to enable the development of scalable and effective data intensive applications, which is also affected by enhanced capacities and extended functionalities of the IT infrastructures. This topic seeks papers in all aspects of distributed and parallel data management and data intensive applications, which are focused around the notions of concurrency, parallelism and distributed processing.

Focus

  • Parallel, replicated, and highly-available distributed databases
  • Data-intensive clouds and grids
  • Middleware for processing large-scale data
  • Service level agreements for data-clouds and grids
  • Distributed and parallel transaction and query processing
  • Management of parallel and distributed data sources
  • Internet-scale data-intensive applications
  • Sensor-network data management
  • Mobile data management
  • Parallel and distributed information retrieval
  • Data-intensive peer-to-peer systems
  • Distributed and cloud-based storage architectures and file systems
  • Parallel data streaming and data stream mining
  • Communication infrastructures for large data sets
  • Algorithms for security and privacy in data management
  • Parallel and distributed knowledge discovery, data mining and integration
  • Data analysis on multi-core and many-core architectures

Topic Committee

Global chair
Maria S. Perez-Hernandez, Universidad Politecnica De Madrid, Spain

Local chair
André Brinkmann, Johannes Gutenberg University Mainz, Germany

Further members
Stergios Anastasiadis, University of Ioannina, Greece
Sandro Fiore, Euro Mediterranean Center on Climate Change and University of Salento, Italy
Adrien Lèbre, Ecole des Mines de Nantes, France
Kostas Magoutis, Foundation for Research and Technology - Hellas, Greece

News

Workshop proceedings published

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