Abstract
The plethora of location-aware devices has led to countless location-based services in which huge amounts of spatiotemporal data get created every day. Several applications require efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Most existing spatiotemporal index structures capture either the current locations of the moving objects or the entire history of the moving objects. Historical spatiotemporal indexing methods require multiple disk I/Os to process new updates and use a discrete trajectory representation that may result in incomplete query results. In this article, we introduce the trails-tree, a disk-based data structure for indexing recent trajectories. The trails-tree requires half the number of disk I/Os needed by other historical spatiotemporal indexing methods for the insertion and querying operations. We give a detailed description of the trails-tree, and we mathematically analyze its performance. Moreover, we present a novel query processing algorithm that ensures the completeness of the query result set. We experimentally verify the performance of the trails-tree using various real and synthetic datasets.