Parallel In-Memory Distance Threshold Queries on Trajectory Databases

Spatiotemporal databases are utilized in many applications to store the trajectories of moving objects. In this context, we focus on in-memory distance threshold queries that return all trajectories found within a distance d of a fixed or moving object over a time interval. We present performance results for a sequential query processing algorithm that uses an in-memory Rtree index, and we find that decreasing index resolution improves query response time. We then develop a simple multithreaded implementation and find that high parallel efficiency (78%-90%) can be achieved in a shared memory environment for a set of queries on a real-world dataset. Finally, we show that a GPGPU approach can achieve a speedup over 3.3 when compared to the multithreaded implementation. This speedup is obtained by abandoning the use of an index-tree altogether. This is an interesting result since index-trees have been the cornerstone of efficiently processing spatiotemporal queries. Keywords-spatiotemporal databases; query parallelization.