CHOROCHRONOS - Research on Spatio-temporal Database Systems

Sellis, Timos

doi:10.1007/3-540-46621-5_18

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In addition, several methods have been proposed to index moving points and trajectories (see [18] for a survey on these specialized methods). All these above methods are extensions of the R-tree, which is based on the 'conservative approximation principle', i.e.…”

Section: Introductionmentioning

confidence: 99%

Overlapping Linear Quadtrees and Spatio-Temporal Query Processing

Tzouramanis¹,

Vassilakopoulos²,

Manolopoulos³

2000

The Computer Journal

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In this paper, indexing in spatio-temporal databases by using the technique of overlapping is investigated. Overlapping has been previously applied in various access methods to combine consecutive structure instances into a single structure, without storing identical sub-structures. In this way, space is saved without sacrificing time performance. A new access method, overlapping linear quadtrees is introduced. This structure is able to store consecutive historical raster images, a database of evolving images. Moreover, it can be used to support query processing in such a database. Five such spatio-temporal queries along with the respective algorithms that take advantage of the properties of the new structure are introduced. The new access method was implemented and extensive experimental studies for space efficiency and query processing performance were conducted. A number of results of these experiments are presented. As far as space is concerned, these results indicate that, in the case of similar consecutive images, considerable storage is saved in comparison to independent linear quadtrees. In the case of query processing, the results indicate that the proposed algorithmic approaches outperform the respective straightforward algorithms, in most cases. The region data sets used in experiments were real images of meteorological satellite views and synthetic random images with specified aggregation.

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Section: Introductionmentioning

confidence: 99%