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Workflows are commonly used to model data intensive scientific analysis. As computational resource needs increase for eScience, emerging platforms like clouds present additional resource choices for scientists and policy makers. We introduce BReW, a tool enables users to make rapid, highlevel platform selection for their workflows using limited workflow knowledge. This helps make informed decisions...
Environmental data arriving constantly from satellites and weather stations are used to compute weather coefficients that are essential for agriculture and viticulture. For example, the reference evapotranspiration (ET0) coefficient, overlaid on regional maps, is provided each day by the California Department of Water Resources to local farmers and turf managers to plan daily water use. Scaling out...
The growing amount of scientific data from sensors and field observations is posing a challenge to ??data valets?? responsible for managing them in data repositories. These repositories built on commodity clusters need to reliably ingest data continuously and ensure its availability to a wide user community. Workflows provide several benefits to modeling data-intensive science applications and many...
Scientific workflows have gained popularity for modeling and executing in silico experiments by scientists for problem-solving. These workflows primarily engage in computation and data transformation tasks to perform scientific analysis in the Science Cloud. Increasingly workflows are gaining use in managing the scientific data when they arrive from external sensors and are prepared for becoming science...
Provenance that captures e-Science activity has long term value only if the right amount and kind of information is collected. In this paper, we propose a two-layer model for representing provenance information capable of representing both execution information and higher level process details. The information model forms the basis for efficient relational database storage and query, and sets the...
Big data presents new challenges to both cluster infrastructure software and parallel application design. We present a set of software services and design principles for data intensive computing with petabyte data sets, named GrayWulf. These services are intended for deployment on a cluster of commodity servers similar to the well-known Beowulf clusters. We use the Pan-STARRS system currently under...
In our demonstration we present Trident, a scientific workflow workbench built on top of a commercial workflow system to leverage existing functionality to the extent possible. Trident is being developed in collaboration with the scientific computing community for use in a number of ongoing eScience projects that make use of scientific workflows, in particular the Pan-STARRS sky survey project and...
Scientific workflows have become an archetype to model in silico experiments in the Cloud by scientists. There is a class of workflows that are used to by "data valets" to prepare raw data from scientific instruments into a science-ready form for use by scientists. These share data-intensive traits with traditional scientific workflows, yet differ significantly, for example, in the required...
Workflows have evolved as the natural tool for scientists to model their eScience experiments. With the scientific world producing data at an explosive rate, workflows have an important part to play in the end to end management of scientific data. To illustrate, workflow can help with fault tolerance and ease of administration when ingesting massive quantities of data using commodity hardware. The...
This work describes an approach to building Grid applications based on the premise that users who wish to access and run these applications prefer to do so without becoming experts on Grid technology. We describe an application architecture based on wrapping user applications and application workflows as Web services and Web service resources. These services are visible to the users and to resource...
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