AIRSTD: An Approach for Indexing and Retrieving Spatio-Temporal Data

Abstract

Geographical (spatial) information about the real world changes rapidly with time. We can simply see examples of these changes when we look at any area. New buildings, new roads and highways, and many other new constructions are added or updated. Spatial changes can be categorized in two categories: (1) Discrete: changes of the geometries of physical entities (i.e., buildings) and (2) abstract: moving objects like airplanes, cars or even moving people. Spatio-temporal databases need to store information about spatial information and record their changes over time. The main goal our study in this paper is to find an efficient way to deal with spatio-temporal data, including the ability to store, retrieve, update, and query. We offer an approach for indexing and retrieving spatio-temporal data (AIRSTD). We concentrate on two main objectives: (1) Provide indexing structures for spatio-temporal data and (2) provide efficient algorithms to deal with these structures.