Methodologies, technologies and applications in distributed and Grid systems.
CONTEXT and GOALS:
Computational Grids, initially used for the sharing of distributed computation resources in scientific applications, start to be used in different application domains offering basic services for application definition and execution in heterogeneous distributed systems.
In health systems, the Grid offers the power and ubiquity needed to the acquisition of biomedical data, processing and delivering of biomedical images (CT, MRI, PET, SPECT, etc) located in different hospitals, within a wide area. So, the Grid acts as a Collaborative Working Environment: doctors often want to aggregate not only medical data, but also human expertise and might want colleague around the world to visualize the examinations in the same way and at the same time so that the group can discuss the diagnosis in real time.
The Grid offers a dynamic infrastructure for retrieving and on-demand processing of remote sensing data, for instance, retrieving of SAR metadata related to terabyte of SAR data, starting on-demand processing on raw data, starting on-demand post-processing on focalized data and creating a complex application composing simple tasks.
For atmospheric and climate modeling, a Grid offers tools for simulate and forecasting meteorological phenomena, simulate emission and dispersion of pollutants for air quality studies and simulate complex phenomena about the impact of global climate changes.
Grid Computing techniques can be used in the motor industry, reducing the optimization process time for improvement of diesel engine emission performance using, for instance, micro-genetic algorithms for engine chamber geometry optimization and Kiva3 code to calculate chamber geometry fitness.
In the computer aided medicine, a new research area involves the use of the Grid technologies for surgical simulations. Some simulations could be performed in a distributed system to allow surgeons to practise executing of particular surgical procedures. Analysis of the problems relevant to the use of GRID in medical virtual environments will be appreciated.
Finally, bioinformatic applications call for the ability to read large datasets (e.g. protein databases) and to create new datasets (e.g. mass spectrometry proteomic data). They can require the ability to change (updating) existing datasets; consequently a Data Grid, i.e. a distributed infrastructure for storing large datasets, is needed. In the bioinformatic field, a Data Grid could reveal useful to build Electronic Patient Record systems (EPRs) for the management of patient information (data, metadata and images), to support data replication, allowing the integration and sharing of biological databases and, generally, for the developement of efficient bioinformatics (in particular proteomic) applications.
The main goal of the Conference Track is to discuss well-known and emerging data-intensive applications in the context of distributed systems and Grid systems, and to analyze technologies and methodologies useful to develop such applications in such environments.
In particular, this Conference Track aims at offering a forum of discussion where young researchers and PhD students could present their research activities, either at an early or mature phase.
The topics of interest include (but are not limited to) the following:
* Data intensive applications in distributed and Grid systems:
* Grid for biomedical imaging;
* Grid for remote sensing and GIS application;
* Grid for Atmospheric and Climate Modeling;
* Grid for motor industry (diesel engine simulation);
* Grid for surgery simulations;
* Bioinformatic for:
* Proteomics and genomics;
* Electronic Patient Records;
* Medical images, data and metadata management;
* Image Recognition, Processing and Analysis.
* Technologies and methodologies in distributed and grid-based applications:
* Grid technologies (Grid portals, Web & Grid services, portlets);
* Grid Information and Monitoring services and related (OO,Relational,XML) data models;
* Grid Security;
* Grid Workload and Data management services;
* Grid Resource management;
* Parallel and Distributed application (cluster and grid based);
* Simulation and Applications of Modeling.
The selected papers after review and extension will be published in a special issue of the Journal of Digital Information Management.
Center for Advanced Computational Technologies
University of Lecce,
Via per Monteroni, 73100 Lecce, Italy
Guest Editors of the special issue:
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|Publication:||Journal of Digital Information Management|
|Date:||Dec 1, 2003|
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