Parallel and Distributed Data Management

  • Rizos Sakellariou
  • Salvatore Orlando
  • Josep Lluis Larriba-Pey
  • Srinivasan Parthasarathy
  • Demetrios Zeinalipour-Yazti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6271)

Abstract

The manipulation and handling of an ever increasing volume of data by current data-intensive applications requires 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 sought papers in all aspects of data management (including databases and data-intensive applications) whose focus relates to some form of parallelism and concurrency.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rizos Sakellariou
  • Salvatore Orlando
  • Josep Lluis Larriba-Pey
  • Srinivasan Parthasarathy
  • Demetrios Zeinalipour-Yazti

There are no affiliations available

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