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Full-Text Articles in Physical Sciences and Mathematics
Optimizing Data Movement In Hybrid Analytic Systems, Patrick Michael Leyshock
Optimizing Data Movement In Hybrid Analytic Systems, Patrick Michael Leyshock
Dissertations and Theses
Hybrid systems for analyzing big data integrate an analytic tool and a dedicated data-management platform, storing data and operating on the data at both components. While hybrid systems have benefits over alternative architectures, in order to be effective, data movement between the two hybrid components must be minimized. Extant hybrid systems either fail to address performance problems stemming from inter-component data movement, or else require the user to explicitly reason about and manage data movement. My work presents the design, implementation, and evaluation of a hybrid analytic system for array-structured data that automatically minimizes data movement between the hybrid components. …
Assessing Satellite Image Data Fusion With Information Theory Metrics, James Cross
Assessing Satellite Image Data Fusion With Information Theory Metrics, James Cross
Dissertations and Theses
A common problem in remote sensing is estimating an image with high spatial and high spectral resolution given separate sources of measurements from satellite instruments, one having each of these desirable properties. This thesis presents a survey of seven families of algorithms which have been developed to provide this common pattern of satellite image data fusion. They are all tested on artificially degraded sets of satellite data from the Moderate Resolution Imaging Spectroradiometer (“MODIS”) with known ideal results, and evaluated using the commonly accepted data fusion assessment metrics spectral angle mapper (“SAM”) and Erreur Relative Globale Adimensionelle de Synth`ese (“ERGAS”). …
Neuroevolution And An Application Of An Agent Based Model For Financial Market, Anil Yaman
Neuroevolution And An Application Of An Agent Based Model For Financial Market, Anil Yaman
Dissertations and Theses
Market prediction is one of the most difficult problems for the machine learning community. Even though, successful trading strategies can be found for the training data using various optimization methods, these strategies usually do not perform well on the test data as expected. Therefore, selection of the correct strategy becomes problematic. In this study, we propose an evolutionary algorithm that produces a variation of trader agents ensuring that the trading strategies they use are different. We discuss that because the selection of the correct strategy is difficult, a variety of agents can be used simultaneously in order to reduce risk. …