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2006
Springer

Stream processing in data-driven computational science

10 years 4 months ago
Stream processing in data-driven computational science
The use of real-time data streams in data-driven computational science is driving the need for stream processing tools that work within the architectural framework of the larger application. Data stream processing systems are beginning to emerge in the commercial space, but these systems fail to address the needs of large-scale scientific applications. In this paper we illustrate the unique needs of large-scale data driven computational science through an example taken from weather prediction and forecasting. We apply a realistic workload from this application against our Calder stream processing system to determine effective throughput, event processing latency, data access scalability, and deployment latency.1
Ying Liu, Nithya N. Vijayakumar, Beth Plale
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where GRID
Authors Ying Liu, Nithya N. Vijayakumar, Beth Plale
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