Affiliations: School of Electrical Engineering and Computer Science,
University of Central Florida, Orlando, FL, USA
Note: [] Corresponding author: Jonathan Cazalas, School of Electrical
Engineering and Computer Science, University of Central Florida, Orlando, FL
32816-2362, USA. E-mail: [email protected]
Abstract: Much research exists for the efficient processing of spatio-temporal
data streams. However, all methods ultimately rely on an ill-equipped
processor [22], namely a CPU, to evaluate concurrent, continuous
spatio-temporal queries over these data streams. This paper presents GEDS, a
scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of
continuous queries over spatio-temporal data streams. Specifically, GEDS
employs the computation sharing and parallel processing paradigms to deliver
scalability in the evaluation of continuous, spatio-temporal range queries and
continuous, spatio-temporal kNN queries. The GEDS framework utilizes the
parallel processing capability of the GPU, a stream processor by trade, to
handle the computation required in this application. Experimental evaluation
shows promising performance and shows the scalability and efficacy of GEDS in
spatio-temporal data streaming environments. Additional performance studies
demonstrate that, even in light of the costs associated with memory transfers,
the parallel processing power provided by GEDS clearly counters and outweighs
any associated costs.
Keywords: Spatio-temporal data streams, computation sharing, parallel processing, location-based services, mobile database systems, continuous query, range query, kNN, graphical processing unit, GPU
