Kerstin Kleese van Dam

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Modern scientific software is daunting in its diversity and complexity. From massively parallel simulations running on the world's largest supercomputers, to visualizations and user support environments that manage ever growing complex data collections, the challenges for software engineers are plentiful. While high performance simulators are necessarily(More)
Data from high-energy physics (HEP) experiments are collected with significant financial and human effort and are mostly unique. An inter-experimental study group on HEP data preservation and long-term analysis was convened as a panel of the International Committee for Future Accelerators (ICFA). The group was formed by large collider-based experiments and(More)
The project aims to provide easy, transparent access to experimental, observational, simulation and visualisation data kept on a multitude of systems and sites. Further more it will provide links to other web/grid services, which will allow the scientists to further use the selected data, e.g. via data mining, simulations or visualisation. The Data Portal(More)
Data services for the Grid have focussed so far primarily on virtualising access to distributed databases, and encapsulating file location. However, orchestration of services requires richer information semantics than these mechanisms provide. Service inputs and outputs must be semantically matched, or characterised in order that sensible transformations(More)
In this paper, we present an approach to large-scale data analysis, Divide and Recombine (D&R), and describe a hardware and software implementation that supports this approach. We then illustrate the use of D&R on large-scale power systems sensor data to perform initial exploration, discover multiple data integrity issues, build and validate algorithms(More)