On past-time indexing of moving objects

Abstract

Tracking of mobile objects trajectories is one of many modern applications supported by Spatiotemporal databases. Within the context of this application, queries about the present, future or past positions of the objects need to be answered. Several indexing methods have been proposed to efficiently handle such spatiotemporal queries. In the current paper, we propose a method for indexing the historic (past) positions of moving objects called XBR-tree, a quadtree-like technique that is able to handle both timestamp and window queries. Moreover, we compare experimentally this with other methods proposed in the literature for the same purpose. In particular, we compare XBR-trees with PMR-trees, structures also related to quadtrees and MV3R-trees, R-tree based structures.

An Experimental Performance Comparison for Indexing Mobile Objects on the Plane

“…One categorization of the aforementioned structures is according to the family of the underlying access method used. In particular, there are approaches based either on R-trees or on Quadtrees as explained in (Raptopoulou, Vassilakopoulos, & Manolopoulos, 2004, 2006. On the other hand, these structures can be also partitioned into those that: (a) are based on geometric duality and represent the stored objects in the dual space (Agarwal, Arge, & Erickson, 2000;Kollios, Gunopulos, & Tsotras, 1999;Patel, Chen, & Chakka, 2004), and (b) leave the original representation intact by indexing data in their native dimensional space (Beckmann, Begel, Schneider, & Seeger, 1990;Papadopoulos, Kollios, Gunopulos, & Tsotras, 2002;Saltenis, Jensen, Leutenegger, & Lopez, 2000;Saltenis et al, 2001;Tao, Papadias, & Sun, 2003).…”

An experimental performance comparison for indexing mobile objects on the plane

“…One categorization of the aforementioned structures is according to the family of the underlying access method used. In particular, there are approaches based either on R-trees or on Quadtrees as explained in [13,14]. On the other hand, these structures can be also partitioned into those that: (a) are based on geometric duality and represent the stored objects in the dual space [1,7,12], and (b) leave the original representation intact by indexing data in their native dimensional space [3,11,15,16,19].…”

A new approach on indexing mobile objects on the plane

“…One categorization of the aforementioned structures is according to the family of the underlying access method used. In particular, there are approaches based either on R-trees or on quadtrees as explained in [32][33][34]. On the other hand, these structures can be also partitioned into (a) those that are based on geometric duality and represent the stored objects in the dual space [3,21,30], and (b) those that leave the original representation intact by indexing data in their native dimensional space [8,29,35,36,40].…”