Markus Schneider
Abstract:
Spatial databases are full-fledged databases that, in addition, enable the storage, retrieval, manipulation, querying, and analysis of geometries like points, lines, and regions representing, for example, the geometries of cities, roads, and states respectively. More complex examples are spatial partitions representing spatial subdivisions like the counties in Florida and the election districts in Gainesville, and spatial graphs representing spatial networks like transportation networks and pipeline systems. Moving objects databases also deal with geometries but focus on the change of their location and/or shape and/or extent over time. Examples are moving points representing cell phone users, moving lines representing traffic congestions, and moving regions representing hurricanes. The objective of this tutorial is to highlight the state of the art of spatial and moving objects databases and indicate their future research challenges. The focus is on data models, querying, data structures, algorithms, and system architectures. The tutorial will show that spatial data types and spatiotemporal data types provide a fundamental abstraction for modeling the geometric structure of objects in space, the temporally evolving structure of objects in space and time, their relationships, properties, and operations. These data types develop their full expressive power if they are integrated as abstract data types into databases and query languages.Tutorial [pdf] (3.94 MB)