Indexing Techniques for Spatiotemporal Databases

We can define as spatiotemporal any database that maintains objects with geometric properties that change over time, where usual geometric properties are the spatial position and spatial extent of an object in a specific d-dimensional space. The need to use spatiotemporal databases appears in a variety of applications such as intelligent transportation systems, cellular communications, and meteorology monitoring. This field of database research collaborates tightly with other research areas such as mobile telecommunications, and is harmonically integrated with other disciplines such as CAD/CAM, GIS, environmental science, and bioinformatics. Spatiotemporal databases stand at the crossroad of two other database research areas: spatial databases (Güting, 1994; Gaede & Gunther, 1998) and temporal databases (Salzberg & Tsotras, 1999). The efficient implementation of spatiotemporal databases needs new data models and query languages and novel access structures for storing and accessing information. In Güting, Bohlen, Erwig, Jensen, Lorentzos, Schneider, and Vazirgiannis (2000) a data model and a query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects, were proposed; the basic idea was to represent time-dependent geometries as attribute data types and to provide an abstract data type extension to the traditional database data models and query languages. In that paper, it was also discussed how various temporal and spatial models could possibly be extended to be spatiotemporal models.