Managing Spatiotemporal Data

Authored by: Sumeet Dua , S. S. Iyengar

Handbook of Data Structures and Applications

Print publication date:  March  2018
Online publication date:  February  2018

Print ISBN: 9781498701853
eBook ISBN: 9781315119335
Adobe ISBN:

10.1201/9781315119335-23

 Download Chapter

 

Abstract

The term spatiotemporal incorporates the two indispensable phenomena of space and time that characterize many objects in the real world. Spatial databases represent, store, and manipulate spatial data in the form of points, lines, areas, surfaces, and hypervolumes in multidimensional space. Most of these databases suffer from, what is commonly called, the “Curse of Dimensionality” [1]. In the literature, curse of dimensionality refers to a performance degradation of similarity queries with increasing dimensionality of these databases. One way to reduce this curse is to develop data structures for indexing such databases for efficient similarity query handling. Specialized data structures such as R-trees and its variants (see Chapter 22) have been proposed for this purpose which have demonstrated multifold performance gains in access time on this data over sequential search. On the other hand, temporal databases store time-variant data.

 Cite
Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.