TY - CHAP
T1 - Scientific process automation and workflow management
AU - Ludäscher, Bertram
AU - Altintas, Ilkay
AU - Bowers, Shawn
AU - Cummings, Julian
AU - Critchlow, Terence
AU - Deelman, Ewa
AU - De Roure, David
AU - Freire, Juliana
AU - Goble, Carole
AU - Jones, Matthew
AU - Klasky, Scott
AU - McPhillips, Timothy
AU - Podhorszki, Norbert
AU - Silva, Claudio
AU - Taylor, Ian
AU - Vouk, Mladen
N1 - Publisher Copyright:
© 2010 by Taylor and Francis Group, LLC.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - Scientific discoveries in the natural sciences are increasingly data driven and computationally intensive, providing unprecedented data analysis and scientific simulation opportunities. To accelerate scientific discovery through advanced computing and information technology, various research programs have been launched in recent years, for example, the SciDAC program by the Department of Energy1 and the Cyberinfrastructure initiative by the National Science Foundation,2 both in the United States. In the UK, the term e-Science3 was coined to describe computationally and data-intensive science, and a large e-Science research program was started there in 2000. With the new opportunities for scientists also come new challenges, for example, managing the enormous amounts of data generated4 and the increasingly sophisticated but also more complex computing environments provided by cluster computers and distributed grid environments. Scientific workflows aim to address many of these challenges.
AB - Scientific discoveries in the natural sciences are increasingly data driven and computationally intensive, providing unprecedented data analysis and scientific simulation opportunities. To accelerate scientific discovery through advanced computing and information technology, various research programs have been launched in recent years, for example, the SciDAC program by the Department of Energy1 and the Cyberinfrastructure initiative by the National Science Foundation,2 both in the United States. In the UK, the term e-Science3 was coined to describe computationally and data-intensive science, and a large e-Science research program was started there in 2000. With the new opportunities for scientists also come new challenges, for example, managing the enormous amounts of data generated4 and the increasingly sophisticated but also more complex computing environments provided by cluster computers and distributed grid environments. Scientific workflows aim to address many of these challenges.
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U2 - 10.1201/9781420069815
DO - 10.1201/9781420069815
M3 - Chapter
AN - SCOPUS:85015240841
SN - 9781420069808
SP - 467
BT - Scientific Data Management
PB - CRC Press
ER -