Topic 05: Parallel and Distributed Data Management


The manipulation and handling of an ever increasing volume of data by current data-intensive applications require novel techniques for efficient data management. Despite recent advances in every aspect of data management (storage, access, querying, analysis, mining), future applications are expected to scale to even higher degrees, not only in terms of volumes of data handled but also in terms of users and resources, often making use of multiple, pre-existing autonomous, distributed or heterogeneous resources.

The notion of parallelism and concurrent execution at all levels remains a key element in achieving scalability and managing efficiently such data-intensive applications, but the changing nature of the underlying environments requires new solutions to cope with such changes.

In this context, this topic seeks papers in all aspects of data management (including databases and data-intensive applications) whose focus relates to some form of parallelism and concurrency.


  • Parallel, replicated, and distributed databases
  • Data-intensive grids and clouds
  • Parallel and distributed algorithms for data mining
  • Middleware for processing large-scale data
  • Distributed and parallel transaction and query processing
  • Parallel and distributed file systems
  • Distributed storage systems
  • Sensor network data management
  • Mobile data management
  • Scalable web services
  • Data management in P2P systems
  • Parallel data streaming
  • Parallel and distributed information retrieval
  • Parallel and distributed knowledge discovery
  • Communication for large data sets
  • Data-intensive applications
  • Parallel and distributed data integration
  • Parallel algorithms for security and privacy in data management

Topic Committee

Global chair

Salvatore Orlando, Università Ca' Foscari Venezia, Italy

Local chair

Gabriel Antoniu, INRIA, France


Amol Ghoting, IBM T. J. Watson Research Center, USA
Maria S. Perez, Universidad Politecnica De Madrid, Spain