ST‐DMQL: A Semantic Trajectory Data Mining Query Language

Abstract

Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.

Keywords:

• Moving objects
• Trajectories
• Semantics
• Trajectory query language
• Trajectory knowledge discovery
• Trajectory data mining

Acknowledgements

This research has been funded by the Brazilian Agency CAPES and the European Union (FP6‐ISTFET Programme Project no. FP6‐14915, GeoPKDD: Geographic Privacy‐Aware Knowledge Discovery and Delivery). We would like to thank Bart Moelans for his comments, and the Traffic Engineering Company of Rio de Janeiro for the trajectory data.