Big Data Stream Analytics and Its Applications

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Presented at IEEEBigDataSecurity2019 2019 by

Prof. Khan

Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining/machine learning techniques in data streams. In addition, we will present a number of stream classification applications such as adaptive website fingerprinting, textual stream analytics (political actor identification over textual stream), attack trace classification using good quality similarity metrics (metric learning) and domain adaptation. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, HPE and Raytheon.