Stream computing is rapidly gaining momentum in markets ranging from healthcare to commercial businesses to defenseâ€”an accelerated adoption driven by the promise of delivering actionable decision support in a timely manner. By processing high-volume data on the wire, we can greatly mitigate the need to rely on traditional paradigms of data warehousing and batch mining of information sources. To deliver on the promise of on-the-wire actionable intelligence, the backend data ingest and routing infrastructure must be supported by advanced analytic algorithms that can extract the value-added information from the stream and enable application-specific analysis and discovery. Amplified by the demand function for stream computing, there exists an immediate need to accelerate the development of analytics for data on the move. We propose to directly address this deficiency by investigating and delivering solutions that significantly advance the state of the art in statistical methodologies at the heart of advanced analytics. In particular, this project seeks to develop novel density estimation techniques that will enable robust data characterization, in an incremental and computationally efficient manner suitable for the streaming paradigm.