Document similarity measures play an important role in many document retrieval and exploration tasks. Over the past decades, several models and techniques have been developed to determine a ranked list of documents similar to a given query document. Interestingly, the proposed approaches typically rely on extensions to the vector space model and are rarely suited for multilingual corpora.

Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we present a system, called ORDEN, that allows for the detection and (visual) exploration of outliers and anomalous events in sensor networks in real-time. In particular, the system constructs outlier regions from anomalous sensor measurements to provide for a comprehensive description of the spatial extent of phenomena of interest. With our system, users can interactively explore displayed outlier regions and investigate the heterogeneity within individual regions using different parameter and threshold settings. Using real-world sensor data streams from different application domains, we demonstrate the effectiveness and utility of our system.