Approximate Query Evaluation Using Linear Constraint Databases Peter Z. Revesz, Rui Chen, and Min Ouyang   View Paper (PDF)   Return to Short Papers 2 Abstract This paper shows that constraint databases can be used for the approximation of several types of discretely recorded continuous data, for example time series data and some spatio-temporal geographic data. We show that time series data can be approximated by a piecewise linear approximation that runs in linear time in the number of data points, and the piecewise linear approximation can be represented in a linear constraint database. Similarly, the spatio-temporal geographic data that is composed of a set of spatial locations, where each location is associated with a time series, can be also approximated and represented in a linear constraint database. The approximations provide data compression, faster query evaluation - that preserve high precision and recall - and interpolation enabling the evaluation of queries that could not be evaluated before. DiSC'02 © 2003 Association for Computing Machinery