We demonstrate our VSOutlier system for supporting interactive exploration of outliers in big data streams. VSOutlier not only supports a rich variety of outlier types supported by innovative and efficient outlier detection strategies, but also provides a rich set of interactive interfaces to explore outliers in real time. Using the stock transactions dataset from the US stock market and the moving objects dataset from MITRE, we demonstrate that the VSOutlier system enables analysts to more efficiently identify, understand, and respond to phenomena of interest in near real-time even when applied to high volume streams.