Big Data Stream Analytics & Its Applications

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 mining. In this talk, I will present an organized picture on how to handle various data mining/machine learning techniques in data streams. In addition, I will present a number of stream applications such as adaptive website fingerprinting,  textual stream classification, new political actor identification over textual stream, and domain adaptation.   

 

This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, and Raytheon.