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    Spatiotemporal anomaly detection: streaming architecture and algorithms

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    https://hdl.handle.net/10217/211792
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    Siegel_colostate_0053A_16155.pdf (4.265Mb)
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    Abstract
    Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of anomaly detection has been extensively studied by mathematicians, statisticians, economists, engineers, and computer scientists. One open research question remains the design of distributed cloud-based architectures and algorithms that can accurately identify anomalies in previously unseen, unlabeled streaming, multivariate spatiotemporal data. With streaming data, time is of the essence, and insights are perishable. Real-world streaming spatiotemporal ...
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    Author(s)
    Siegel, Barry W.

    Advisor(s)
    Labadie, John

    Date Issued
    2020
    Format
    born digital; doctoral dissertations
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    • 2020- CSU Theses and Dissertations
    • Theses and Dissertations - Department of Systems Engineering

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