Douglas E. Galarus

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In this paper, we investigate the application of data mining to existing techniques for quality control/anomaly detection on weather sensor observations. Specifically we adapt the popular Barnes Spatial interpolation method to use time series distance rather than spatial distance to develop an online algorithm that uses readings from similar stations based(More)
Neighborhoods, as used for spatial and spatial-temporal data mining, define areas of similarity in data. Unless defined to account for outliers, missing data and spatial-temporal variation, the robustness of methods utilizing neighborhoods will suffer. The focus of this paper is to demonstrate that neighborhoods can be defined and used in a robust manner(More)
Quality control for near-real-time spatial-temporal data is often presented from the perspective of the original owner and provider of the data, and focuses on general techniques for outlier detection or uses domain-specific knowledge and rules to assess quality. The impact of quality control on the data aggregator and redistributor is neglected. The focus(More)
A significant challenge we face in assessing spatio-temporal data quality is a lack of ground-truth data. Error is by definition the deviation of observation from ground truth. In the absence of ground truth, we depend on our own or provider quality assessment to evaluate our methods. The focus of this paper is the development of a representative,(More)
©COPYRIGHT by Douglas Edward Galarus 2015 All Rights Reserved ii DEDICATION The work of my PhD is dedicated to my family in gratitude for their patience during the time that I worked on it. It has been especially hard for my small children to understand why I am constantly needing time to do " school work " , and their lives to date have (Michael) or nearly(More)
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