We present the design and implementation of a component for the preprocessing of position data taken from moving objects. The movement of mobile objects is represented by piecewise functions over time that approximate the real object movement and significantly reduce the initial data volume such that efficient storage and analysis of object trajectories can be achieved. The maximal acceptable deviation---an input parameter of our algorithms---of the approximations also includes the uncertainty of the position sensor measurements. We analyze and compare five different lossy preprocessing methods. Our results clearly indicate that even with simple approaches, a more than sufficient overall performance can be achieved.