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)
E-science projects of various disciplines face a fundamental challenge: thousands of users want to obtain new scientific results by applicationspecific and dynamic correlation of data from globally distributed sources. Considering the involved enormous and exponentially growing data volumes, centralized data management reaches its limits. Since scientific(More)
E-Science Projekte vieler Fachrichtungen sehen sich mit den Herausforderungen einer stark wachsenden Datenflut konfrontiert. Die anwendungsspezifische, dynamische Fusion logisch verwandter Daten aus weltweit verteilten Quellen ist von höchstem wissenschaftlichen Interesse. Eine herkömmliche zentrale Datenhaltung stößt hierbei allerdings auf Grund der(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, collaborative data management by employing dominant data characteristics (e. g., data skew) and query patterns to(More)
We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or(More)
Über die bereits vorhandenen Datenvolumina hinaus, stellt insbesondere die antizipierte Datenflut neuer e-ScienceProjekte Forscher vor neue Herausforderungen. Im Rahmen des AstroGrid-D-Projektes der deutschen Grid-Initiative (DGrid) erforschen wir mit dem HiSbase-System ein durchsatzoptimiertes Datenmanagement für fachspezifische Forschungsverbünde, das wir(More)