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.
