Grid-based Indexing of a Newswire Corpus
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
ACSW Frontiers '05 Proceedings of the 2005 Australasian workshop on Grid computing and e-research - Volume 44
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
Portfolio and investment risk analysis on global grids
Journal of Computer and System Sciences
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
A virtual laboratory for medical image analysis
IEEE Transactions on Information Technology in Biomedicine
Task profiling model for load profile prediction
Future Generation Computer Systems
Australian neuroinformatics research – grid computing and e-research
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
On-Line task granularity adaptation for dynamic grid applications
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Task granularity policies for deploying bag-of-task applications on global grids
Future Generation Computer Systems
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The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testbed resources are presented along with their graphical visualization. Copyright © 2005 John Wiley & Sons, Ltd.