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)
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)
In this prospectus, we investigate the impact of various data quality factors on the problem of determining data quality for observations from a given weather sensor data stream. Our problem is an offshoot of various research and development projects conducted at the Western Transportation Institute for the California Department of Transportation (Caltrans)(More)
There is a need for comprehensive solutions to address the challenges of spatio-temporal data quality assessment. Emphasis is often placed on the quality assessment of individual observations from sensors but not on the sensors themselves nor upon site metadata such as location and timestamps. The focus of this paper is on the development and evaluation of(More)
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