Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Mining trajectory databases (TD) has gained great interest due to the popularity of tracking devices. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the centroid trajectory of a group of movements. Third, we propose a variant of the fuzzy C-means (FCM) clustering algorithm, which embodies CenTra at its update procedure. The experimental evaluation over real world TD demonstrates the efficiency and effectiveness of our approach.

An emerging topic in the field of spatial data management is the handling of location uncertainty of spatial objects, mainly due to inaccurate measurements. The literature on location uncertainty so far has focused on modifying traditional spatial search algorithms in order to handle the impact of objects' location uncertainty in query results. In this paper, we present the first, to the best of our knowledge, theoretical analysis that estimates the average number of false hits introduced in the results of rectangular range queries in the case of data points uniformly distributed in 2D space. Then, we relax the original distribution assumptions showing how to deal with arbitrarily distributed data points and more realistic location uncertainty distributions. The accuracy of the results of our analytical approach is demonstrated through an extensive experimental study using various synthetic and real datasets. Our proposal can be directly employed in spatial database systems in order to provide users with the accuracy of spatial query results based only on known dataset and query parameters.

We present HERMES, a prototype system based on a powerful query language for trajectory databases, which enables the support of aggregative Location-Based Services (LBS). The key observation that motivates HERMES is that the more the knowledge in hand about the trajectory of a mobile user, the better the exploitation of the advances in spatio-temporal query processing for providing intelligent LBS. HERMES is fully incorporated into a state-of-the-art Object-Relational DBMS, and its demonstration illustrates its flexibility and usefulness for delivering custom-defined LBS.

The problem of trajectory similarity in moving object databases is a relatively new topic in the spatial and spatiotemporal database literature. Existing work focuses on the spatial notion of similarity ignoring the temporal dimension of trajectories and disregarding the presence of a general-purpose spatiotemporal index. In this work, we address the issue of spatiotemporal trajectory similarity search by defining a similarity metric, proposing an efficient approximation method to reduce its calculation cost, and developing novel metrics and heuristics to support k-most-similar-trajectory search in spatiotemporal databases exploiting on existing R-tree-like structures that are already found there to support more traditional queries. Our experimental study, based on real and synthetic datasets, verifies that the proposed similarity metric efficiently retrieves spatiotemporally similar trajectories in cases where related work fails, while at the same time the proposed algorithm is shown to be efficient and highly scalable.