Today, a number of positioning technologies exist in order to track moving objects. While GPS devices enable wayfinding in outdoor environments, several techniques have been devised for indoor tracking, to enable smart spaces, for example. But even at the microscopic scale objects are tracked by researchers of the natural sciences with imaging technologies. Regardless of the spatial scale and application at hand, a common problem consists in the ever growing quantities of movement data which are to be managed. One strategy asks for how to simplify the data, such that compact representations save space but do still capture relevant information. Such an abstraction is described in this paper. It is shown how it can be applied to constraint programming techniques in order to search for movement patterns of groups of objects. Instead of exhaustively searching by means of
generate and test
, the representation allows the application of
. As a consequence, search space can be reduced significantly. Moreover, it is shown how the chosen representation aids the dealing with a specific class of imprecise data. The domain of biological cells is used for illustrating the presented methods. The resulting observations, made by light microscopes, suffer from the addressed class of imprecise data.