Literature Review on Temporal, Spatial, and Spatiotermpoal Data Models

This paper reviews papers on temporal databases, spatial databases, and spatio-temporal databases

Affine-Invariant Triangulation of Spatio-Temporal Data with an Application to Image Retrieval

In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz , spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying and animation of spatio-temporal data, we present a normal form for data in the geometric data model. We propose an algorithm for constructing this normal form via a spatio-temporal triangulation of geometric data objects. This triangulation algorithm generates new geometric data objects that partition the given objects both in space and in time. A particular property of the proposed partition is that it is invariant under time-dependent affine transformations, and hence independent of the particular choice of coordinate system used to describe the spatio-temporal data in. We can show that our algorithm works correctly and has a polynomial time complexity (of reasonably low degree in the number of input triangles and the maximal degree of the polynomial functions that describe the transformation functions). We also discuss several possible applications of this spatio-temporal triangulation. The application of our affine-invariant spatial triangulation method to image indexing and retrieval is discussed and an experimental evaluation is given in the context of bird images

A parametric prototype for spatiotemporal databases

The main goal of this project is to design and implement the parametric database (ParaDB). Conceptually, ParaDB consists of the parametric data model (ParaDM) and the parametric structured query language (ParaSQL). Parametric data model is a data model for multi-dimensional databases such as temporal, spatial, spatiotemporal, or multi-level secure databases. Main difference compared to the classical relational data model is that ParaDM models an object as a single tuple, and an attribute is defined as a function from parametric elements. The set of parametric elements is closed under union, intersection, and complementation. These operations are counterparts of or, and, and not in a natural language like English. Therefore, the closure properties provide very flexible ways to query on objects without introducing additional self-join operations which are frequently required in other multi-dimensional database models

A Theory of Spatio-Temporal Database Queries

Abstract. We address a fundamental question concerning spatio-temporal database systems: “What are exactly spatio-temporal queries? ” We define spatio-temporal queries to be computable mappings that are also generic, meaning that the result of a query may only depend to a limited extent on the actual internal representation of the spatio-temporal data. Genericity is defined as invariance under transformations that preserve certain characteristics of spatio-temporal data (e.g., collinearity, distance, velocity, acceleration,...) that are relevant to a database user. These transformations also respect the monotone nature of time. We investigate different genericity classes relative to the constraint database model for spatio-temporal databases and we identify sound and complete languages for the first-order, respectively the computable, queries in these genericity classes.