Anomaly detection in massive sensor collections. The monitoring of urban, societal and environmental variables like air pollution level, mobile phone activity, or tropical vegetation cover through various sensors allows to detect interesting, unusual events in near real-time. In this project, novel approaches to capture and analyze complex dynamics within massive collections of time-series of such sensor readings are explored to reveal anomalous events. A special focus is put on changes in the correlation structure of the time-series as well as the different spatial scales of events—ranging from small events with localized impacts to massive events that lead to global anomalies. The project combines methods from anomaly detection, correlation tracking and combinatorial optimization. Image: Ratti et al. 2006.