Trajectory Pattern Mining for Urban Computing in the Cloud

作者：Altomare Albino^*; Cesario Eugenio; Comito Carmela; Marozzo Fabrizio; Talia Domenico

来源：IEEE Transactions on Parallel and Distributed Systems, 2017, 28(2): 586-599.

DOI：10.1109/TPDS.2016.2565480

• Trajectory mining
• urban computing
• cloud computing
• distributed data

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 data can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. From a technological viewpoint, Cloud computing can play an essential role by helping city administrators to quickly acquire new capabilities and reducing initial capital costs by means of a comprehensive pay-as-you-go solution. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, in a Cloud-based framework. Experimental evaluation has been carried out on both real-world and synthetic trajectory data, up to one million of trajectories. The results show that, due to the high complexity and large volumes of data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture in terms of both execution time, speed-up and scale-up.

• 出版日期2017-2