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• W2922778942 abstract "In this paper, we demonstrate the difficulty of conducting spatio-temporal data quality control for sensor data. Our motivation is the provision of quality traveler information by departments of transportation. We show that assessment of accuracy of air temperature requires robust methods that go beyond the identification of outliers and inliers to mitigate the impact of bad data and bad metadata. We give a representative approach and demonstrate the challenges of assessment, particularly in the presence of incorrect data quality labels and the absence of ground truth for this air temperature data. Our approach is model-based and can be used to estimate not only outliers versus inliers, but also degree of outlyingness. It can not only be used to identify bad data in general as well as bad metadata. We evaluate our approach against other methods that use interpolation to model the data. We use an Area Under the ROC (AUROC) analysis to compare methods when data quality labels are provided. We use mean-squared-error and t-tests to compare methods both when labels are provided and when not. We measure scalability using computation time." @default.
• W2922778942 created "2019-04-01" @default.
• W2922778942 creator A5009847987 @default.
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• W2922778942 date "2019-02-02" @default.
• W2922778942 modified "2023-10-14" @default.
• W2922778942 title "Accurate, Timely, Reliable: A High Standard and Elusive Goal for Traveler Information Data Quality" @default.
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• W2922778942 doi "https://doi.org/10.1007/978-3-030-12388-8_41" @default.
• W2922778942 hasPublicationYear "2019" @default.
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