Visually exploring movement data via similarity-based analysis

摘要

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for “hands-on” analysis for trajectory data.