Tobias Scholl

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The field of e-science currently faces many challenges. Among the most important ones are the analysis of huge volumes of scientific data and the connection of various sciences and communities, thus enabling scientists to share scientific interests, data, and research results. These issues can be addressed by processing large data volumes on-thefly in the(More)
In federated Data Grids, individual institutions share their data sets within a community to enable collaborative data analysis. Data access needs to be provided in a scalable fashion since in most e-science communities, data sets do not only grow exponentially but also experience an increasing popularity. If data autonomy is retained, each individual(More)
Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Abstract E-science projects of various disciplines face a fundamental(More)
Collaborative research in various scientific disciplines requires support for scalable data management enabling the efficient correlation of globally distributed data sources. Motivated by the expected data rates of upcoming projects and a growing number of users, communities explore new data management techniques for achieving high throughput.(More)
E-science communities face huge data management challenges due to large existing data sets and expected data rates from forthcoming projects. Community-driven data grids provide a scalable, high-throughput oriented data management solution for scientific federations by employing domain-specific partitioning schemes and parallelism. In this paper, we present(More)
Beyond already existing huge data volumes, e-science communities face major challenges in managing the anticipated data deluge of forthcoming projects. Community-driven data grids target at domain-specific federations and provide a distributed, collabo-rative data management by employing dominant data characteristics (e. g., data skew) and query patterns to(More)
A comprehensive study for a sensitivity optimization in MCE with mass spectrometric detection is presented. As a text mixture, we chose a mixture of the cardiac drugs propranolol, bisoprolol, lidocaine, procaine and studied the effect of different chip layouts and experimental parameters with the aim of achieving both high sensitivity in MS detection and(More)