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[Myexperiment-discuss] [Fwd: europar]
From: |
Paolo Missier |
Subject: |
[Myexperiment-discuss] [Fwd: europar] |
Date: |
Tue, 12 Jan 2010 08:35:56 +0000 |
User-agent: |
Thunderbird 2.0.0.23 (Macintosh/20090812) |
Hi,
posted on Rizos' behalf (apologies for cross-posting)
-Paolo
======================================================
CALL FOR PAPERS
Topic 5: Parallel and Distributed Data Management
Euro-Par 2010, Ischia, Italy, Aug.31st-Sep.3rd, 2010
Abstracts due: January 31st, 2010
Papers due: February 7th, 2010
http://www.europar2010.org/
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 seeks papers in all aspects of data
management (including databases and data-intensive applications)
whose focus relates to some form of parallelism and concurrency.
Focus:
* 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 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
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