Stream-Mode FPGA Acceleration of Complex Pattern Trajectory Querying

The wide and increasing availability of collected data in the form of trajectory has lead to research advances in behavioral aspects of the monitored subjects (e.g., wild animals, people, vehicles). Using trajectory data harvested by devices, such as GPS, RFID and mobile devices, complex pattern queries can be posed to select trajectories based on specific events of interest. In this paper, we present a study on FPGA-based architectures processing complex patterns on streams of spatio-temporal data. Complex patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. More importantly, variables can be used to substantially enhance the flexibility and expressive power of pattern queries. Here we explore the challenges in handling several constructs of the assumed pattern query language, with a study on the trade-offs between expressiveness, scalability and matching accuracy. We show an extensive performance evaluation where FPGA setups outperform the current state-of-the-art CPU-based approaches by over three orders of magnitude. Unlike software-based approaches, the performance of the proposed FPGA solution is only minimally affected by the increased pattern complexity.