Trajectory Pattern Mining over a Cloud-Based Framework for Urban Computing

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, and sensors, allows for the collections of large amounts of movement data. This amount of information can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, executed on a Cloud-based framework for urban computing. Experimental evaluation shows that, due to complexity and large data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture.