Bart Moelans

A model for enriching trajectories with semantic geographical information

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a users perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.

A Survey of Spatio-Temporal Data Warehousing

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.

Qualitative polyline similarity testing with applications to query-by-sketch, indexing and classification

We present an algorithm for polyline (and polygon) similarity testing that is based on the double-cross formalism. To determine the degree of similarity between two polylines, the algorithm first computes their generalized polygons, that consist of almost equally long line segments and that approximate the length of the given polylines within an

Privacy protection: Regulations and technologies, opportunities and threats

varepsilon

Analyzing Trajectories Using Uncertainty and Background Information

-error margin. Next, the algorithm determines the double-cross matrices of the generalized polylines and the difference between these matrices is used as a measure of dissimilarity between the given polylines. We prove termination of our algorithm and show that its sequential time complexity is bounded by

Map matching and uncertainty: an algorithm and real-world experiments

Oleft((fracmax(N_1,N_2)varepsilon)^2right)

Wireless Network Data Sources: Tracking and Synthesizing Trajectories

, where

Uncertainty-Based Map Matching: The Space-Time Prism and k-Shortest Path Algorithm

N_1

Towards a geometric interpretation of double-cross matrix-based similarity of polylines

and

On the realisability of double-cross matrices by polylines in the plane

N_2