Analyzing Trajectories Using Uncertainty and Background Information

A key issue in clustering data, regardless the algorithm used, is the definition of a distance function. In the case of trajectory data, different distance functions have been proposed, with different degrees of complexity. All these measures assume that trajectories are error-free, which is essentially not true. Uncertainty is present in trajectory data, which is usually obtained through a series of GPS of GSM observations. Trajectories are then reconstructed, typically using linear interpolation. A well-known model to deal with uncertainty in a trajectory sample, uses the notion of space-time prisms (also called beads), to estimate the positions where the object could have been, given a maximum speed. Thus, we can replace a (reconstructed) trajectory by a necklace (intuitively, a a chain of prisms), connecting consecutive trajectory sample points. When it comes to clustering, the notion of uncertainty requires appropriate distance functions. The main contribution of this paper is t...

• Type: Conference
• Year: 2009
• Where: SSD
• Authors: Bart Kuijpers, Bart Moelans, Walied Othman, Alejandro A. Vaisman
• Keywords: Database, Distance Function, SSD 2009, Trajectory, Trajectory Samples