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OGSA-DAI provides an extensible Web service-based framework that allows data resources to be incorporated into Grid fabrics. The current OGSA-DAI release (OGSA-DAI WSI/WSRF v2.2) has implemented a set of optimizations identified through the examination of common OGSA-DAI use patterns. In this paper we describe these patterns and detail the optimizations(More)
OGSA-DAI (Open Grid Services Architecture Data Access and Integration) is a framework for building distributed data access and integration systems. Until recently, it lacked the built-in functionality that would allow easy creation of federations of distributed data sources. The latest release of the OGSA-DAI framework introduced the OGSA-DAI DQP(More)
OGSA-DAI provides an extensible framework that allows data resources to be incorporated into Grid fabrics. The current OGSA-DAI release, version 3.0, is a complete top-to-bottom redesign and implementation of the OGSA-DAI product. A number of fundamental conceptual and design changes are introduced in this release. In this paper we describe the motivation(More)
The statistical language R and Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by these analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing (HPC) systems offer a solution to this issue. The Simple Parallel R INTerface(More)
Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive,(More)
R is a free statistical programming language commonly used for the analysis of high-throughput microarray and other data. It is currently unable to easily utilise multiprocessor architectures without substantial changes to existing R scripts. Further, working with large volumes of data often leads to slow processing and even memory allocation faults. A(More)
As large grid infrastructures, such as Enabling Grids for E-sciencE, mature, they are being used by scientists around the world in their daily work, running thousands of concurrent computational jobs and transferring large amounts of data. The successful and sustainable operation of such grid infrastructures is only possible through the use of monitoring(More)