, Volume 19, Issue 2, pp 405–434 | Cite as

High performance FPGA and GPU complex pattern matching over spatio-temporal streams

  • Roger MoussalliEmail author
  • Ildar Absalyamov
  • Marcos R. Vieira
  • Walid Najjar
  • Vassilis J. Tsotras


The wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g., wild animals, people, and 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- and GPU-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 and GPU setups outperform the current state-of-the-art (single-threaded) CPU-based approaches, by over three orders of magnitude for FPGAs (for expressive queries) and up to two orders of magnitude for certain datasets on GPUs (and in some cases slowdown). Unlike software-based approaches, the performance of the proposed FPGA and GPU solutions is only minimally affected by the increased pattern complexity.


Spatio-temporal Spatial Temporal Database FPGA GPU Acceleration Pattern Matching 



This work has been partially supported by National Science Foundation awards: CCF-1219180, IIS-1161997 and IIS-1305253.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Roger Moussalli
    • 1
    Email author
  • Ildar Absalyamov
    • 2
  • Marcos R. Vieira
    • 3
  • Walid Najjar
    • 2
  • Vassilis J. Tsotras
    • 2
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.Department of Computer Science and EngineeringUniversity of California, RiversideRiversideUSA
  3. 3.IBM Research - BrazilRio de JaneiroBrazil

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