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In information integration tasks, rats applied what they learned more quickly.
In this thesis, we study approaches for different data integration tasks that combine machine algorithms and human intelligence.
In particular, we focus on three data integration tasks: entity resolution, top-k item detection, and data aggregation.
We develop a broad theoretical framework for modelling difficult perceptual information integration tasks under different decision rules.
As the Pentagon found out in trying to integrate computer systems for control across a whole theater, and NASA learned while putting together smaller systems for the International Space Station, predictability declines as the integration tasks become larger and larger.
The costs for standard spacecraft testing and integration tasks are included, but not those of launch, ground support, operations and engineer working hours.
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This process integration task provides several opportunities for the application of systematic decision making methodologies.
We apply the models to data from a perceptual integration task with near threshold stimuli under two different decision rules.
However, determination of a complete and consistent set of data transformations for any given integration task is far from trivial.
Engineering of Human Machine Interfaces for supervisory control in process industries can be characterized as an information integration task.
In this sense, knowledge management technologies have proved to be highly promising for supporting this integration task.
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