Now showing items 1-2 of 2
Framework for real-time, autonomous anomaly detection over voluminous time-series geospatial data streams, A
Format:born digital; masters theses
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 ...
Leveraging ensembles: balancing timeliness and accuracy for model training over voluminous datasets
Format:born digital; doctoral dissertations
As data volumes increase, there is a pressing need to make sense of the data in a timely fashion. Voluminous datasets are often high dimensional, with individual data points representing a vector of features. Data scientists ...