ROAM: Rule-and motif-based anomaly detection in massive moving object data sets

With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spa-tiotemporal features.