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    Framework for real-time, autonomous anomaly detection over voluminous time-series geospatial data streams, A

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    http://hdl.handle.net/10217/83908
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    Abstract
    In this research work we present an approach encompassing both algorithm and system design to detect anomalies in data streams. Individual observations within these streams are multidimensional, with each dimension corresponding to a feature of interest. We consider time-series geospatial datasets generated by remote and in situ observational devices. Three aspects make this problem particularly challenging: (1) the cumulative volume and rates of data arrivals, (2) anomalies evolve over time, and (3) there are spatio-temporal correlations associated with the data. Therefore, anomaly detections ...
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    Author(s)
    Budgaga, Walid

    Advisor(s)
    Pallickara, Shrideep; Pallickara, Sangmi Lee

    Date Issued
    2014
    Format
    born digital; masters theses
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Computer Science

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