Existing spatiotemporal indexes suffer from either large update cost or poor query performance, except for the Bx-tree (the state-of-the-art), which consists of multiple B+-trees indexing the 1D values transformed from the (multi-dimensional) moving objects based on a space filling curve (Hilbert, in particular). This curve, however, does not consider object velocities, and as a result, query processing with a Bx-tree retrieves a large number of false hits, which seriously compromises its efficiency. It is natural to wonder “can we obtain better performance by capturing also the velocity information, using a Hilbert curve of a higher dimensionality?”. This paper provides a positive answer by developing the Bdual-tree, a novel spatiotemporal access method leveraging pure relational methodology. We show, with theoretical evidence, that the Bdual-tree indeed outperforms the Bx-tree in most circum- stances. Furthermore, our technique can effectively answer progressive spatiotemporal queries, which are poorly supported by Bx-trees.
