We present a new compressed representation of free trajectories of moving objects. It combines a partial-sums-based structure that retrieves in constant time the position of the object at any instant, with a hierarchical minimum-bounding-boxes representation that allows determining if the object is seen in a certain rectangular area during a time period. Combined with spatial snapshots at regular intervals, the representation is shown to outperform classical ones by orders of magnitude in space, and also to outperform previous compressed representations in time performance, when using the same amount of space.
Multimedia applications usually involve a large number of multimedia objects (texts, images, sounds, etc.). An important issue in this context is the specification of spatial and temporal relationships among these objects. In this paper we define such a model, based on a set of spatial and temporal relationships between objects participating in multimedia applications. Our work exploits existing approaches for spatial and temporal relationships. We extend these relationships in order to cover the specific requirements of multimedia applications and we integrate the results in a uniform framework for spatio-temporal composition representation. Another issue is the efficient handling of queries related to the spatio-temporal relationships among the objects during the authoring process. Such queries may be very costly and appropriate indexing schemes are needed so as to handle them efficiently. We propose efficient such schemes, based on multidimensional (spatial) data structures, for large multimedia applications that involve thousands of objects. Evaluation models of the proposed schemes are also presented, as well as hints for the selection of the most appropriate one, according to the multimedia author's requirements.  With the rapid increase in the use of inexpensive, location-aware sensors in a variety of new applications, large amounts of time-sequenced location data will soon be accumulated. Efficient indexing techniques for managing these large volumes of trajectory data sets are urgently needed. The key requirements for a good trajectory indexing technique is that it must support both searches and inserts efficiently. This paper proposes a new indexing mechanism called SETI, a Scalable and Efficient Trajectory Index, that meets these requirements. SETI uses a simple two-level index structure to decouple the indexing of the spatial and the temporal dimensions. This decoupling makes both searches and inserts very efficient. Based on an actual implementation, we demonstrate that SETI clearly outperforms two previously proposed trajectory indexing mechanisms, namely the 3D R-tree and the TB-tree. Unlike previously proposed trajectory indexing structures, SETI is a logical indexing structure that uses existing spatial indexing structures, such as R-trees, without any modifications. Consequently, DBMSs that currently support R-trees can easily implement SETI, making it a both a practical and an efficient choice for indexing trajectory data sets.