Nowadays, an abundance of sensors are used to collect very large datasets of moving objects. The movement of these objects can be analysed by identifying common routes. For this, a cluster of trajectories must be defined and the pattern of each cluster discovered. In this article, we introduce a new pattern, called the Trajectory Box Plot (TBP), to summarize a set of trajectories following the same route. The TBP is an extension of the well-known descriptive statistics Box Plot concept. Each TBP is described by a median trajectory, a 3D box and a 3D fence. The median trajectory depicts the typical movement of mobile objects. The box and the fences (whiskers) describe the spatial and temporal spreading around the central tendency. TBPs are useful to summarize and analyse trajectory streams, understand their spatio-temporal density and detect outliers. In this article, visual analysis highlights how the TBP pattern effectively describes how the density of trajectory clusters change over time.

Mining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however do not scale to real-life dataset sizes. Therefore a distributed algorithm for convoy mining is inevitable. In this paper, we discuss the problem of convoy mining and analyze different data partitioning strategies to pave the way for a generic distributed convoy pattern mining algorithm.

This paper investigates the problems that arise when application requirements dictate that autonomous spatial databases be integrated into a federated one. The paper focuses on the most critical issues raised by the integration of databases of different scales. A short presentation of approaches to interoperability and of the main steps composing the integration process is given first. Next, a general format is proposed for precisely defining correspondences between objects of two databases. The format can deal with a wide range of discrepancies in GIS data. Last, a solution is presented for aggregation conflicts which arise when one object of one database corresponds to a set of objects in the other database, a very frequent case when the databases are of different scales. The method is applied to excerpts of real cartographic databases.