We will demonstrate our system, called V iStream, supporting interactive visual exploration of neighbor-based patterns [7] in data streams. V iStream does not only apply innovative multi-query strategies to compute a broad range of popular patterns, such as clusters and outliers, in a highly efficient manner, but it also provides a rich set of visual interfaces and interactions to enable real-time pattern exploration. With ViStream, analysts can easily interact with pattern mining processes by navigating along the time horizons, abstraction levels and parameter spaces, and thus better understand the phenomena of interest.

In this work, we describe our approach for making the interactive data exploration system, called XmdvTool, quality-aware to assure informed decision-making. XmdvToolQ, makes quality or lack thereof explicit for all stages of the data exploration process from raw data, to abstracted data, to the final visual displays, allowing users to query and navigate through data-, structure- and quality-spaces.