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An important need for users of big data systems is to get an answer within a specified timebound. A current limitation of workflow systems is that they do not take into account user deadlines: they run workflows selected by the user, but take their time to do so. This is impractical when large datasets are at stake, since users often prefer to see an answer faster even if it has lower precision or quality. We recently developed WOOT, an extension to workflow systems that enables them to take into account user deadlines by automatically generating alternative workflow candidates and ranking them according to performance estimates. The WOOT system makes these estimates based on workflow performance models created from workflow executions, and uses semantic technologies to reason about workflow options. Possible workflow candidates are presented to the user in a compact manner, and are ranked according to their runtime estimates. WOOT combines and extends capabilities from the WINGS semantic workflow system and the Apache OODT Object Oriented Data Technology and workflow execution system.
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