Efficient processing of past-future spatiotemporal queries

Abstract

Spatiotemporal databases emerge as an evolving scientific field due to a great variety of applications, tracking mobile objects being one of them. For this purpose, a number of methods have been proposed to efficiently organize and index moving objects and answer spatiotemporal queries. The majority of all these methods are addressing either the past or the future movement of the moving objects. Up until now, addressing both the past and the future movement of the objects in an integrated manner has rarely appeared in the literature. In the current paper, based on a spatiotemporal access method, the XBR-tree, we propose algorithms for the efficient processing of spatiotemporal window (past) and timestamp (past, present and future) queries. Moreover, we experimentally study the efficiency of processing these queries based on the XBR-tree against using an existing structure, the R P P F -tree.

HTPR*-Tree: An Efficient Index for Moving Objects to Support Predictive Query and Partial History Query

“…Most of these index structures are classified into two categories; one of them is to handle past positions or trajectories [4][5][6], and the other is to handle current and future positions [1,2,[7][8][9][10][11][12]. In addition, some indices suitable for history and future queries of moving objects have also been studied [13,14]. History trajectories indices such as STR-tree and TB-tree [4] are used to index positions for an object only up to the time of the most recent sample.…”

“…The implemented query types are only timestamp ones. Raptopoulou [14] et al extended the XBR-tree to deal with future prediction as well. But it only can support timestamp queries involved from the past to the future and history window queries.…”

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].…”

Βάσεις Δεδομένων Για Την Αποθήκευση Και Την Επεξεργασία Κινούμενων Σημείων

“…Το Κεφάλαιο 5 βασίζεται στην εργασία [49], ενώ το Κεφάλαιο 6 στην εργασία [52]. Τέλος, στο Κεφάλαιο 7 βασίζεται στην εργασία [53]. Στο Παράρτηµα της διατριβής παρατίθεται πλήρης λίστα των εργασιών, που προέκυψαν κατά τη διάρκεια εκπόνησης της διατριβής αυτής.…”