The ability of determining and dealing with the trajectories followed by an object in a given (concrete or abstract) space turns out to be quite useful in a variety of contexts. This is the case, in particular, in positioning, where it can be exploited, for instance, for traffic control and user profiling. A key step in trajectory management is the evaluation of trajectory similarity. In many positioning applications, trajectories are built from Global Navigation Satellite System (GNSS) readings; however, in various scenarios, these coordinates are not available. In this paper, we focus on fingerprint positioning systems characterised by a low sampling frequency and a high heterogeneity of the observations. We start with a comprehensive analysis of well-known GNSS-based trajectory similarity measures, and show how some of them can actually be adapted to the fingerprinting setting. Then, we outline a novel approach that exploits multiple information, including both spatial and cellular identifiers with received signal strength. Finally, we make an extensive, experimental comparative evaluation of the various measures (adapted and novel ones) over a real-world fingerprint dataset.

We propose a mapping from a database conceptual design to a schema for XML that produces highly connected and nested XML structures. We first introduce two alternative definitions of the mapping, one modeling entities as global XML elements and expressing relationships among them in terms of keys and key references (flat design), the other one encoding relationships by properly including the elements for some entities into the elements for other entities (nest design). Then we provide a benchmark evaluation of the two solutions showing that the nest approach, compared to the flat one, leads to improvements in both query and validation performances. This motivates us to systematically investigate the best way to nest XML structures. We identify two different nesting solutions: a maximum depth nesting, that keeps low the number of costly join operations that are necessary to reconstruct information at query time using the mapped schema, and a maximum density nesting, that minimizes the number of schema constraints used in the mapping of the conceptual schema, thus reducing the validation overhead. On the one hand, the problem of finding a maximum depth nesting turns out to be NP-complete and, moreover, it admits no constant ratio approximation algorithm. On the other hand, we devise a graph-theoretic algorithm, NiduX, that solves the maximum density problem in linear time. Interestingly, NiduX finds the optimal solution for the harder maximum depth problem whenever the conceptual design graph is either acyclic or complete. In randomly generated intermediate cases of the graph topology, we experimentally show that NiduX finds a good approximation of the optimal solution.

Dealing with multiple representations of the same spatial data is widely recognized as a relevant problem in conceptual modeling of spatial information. Unfortunately, existing solutions provide no support to the management of consistency constraints on multiple representations. In this paper, we propose an extension to the ChronoGeoGraph (CGG) conceptual model, which features both a relation of spatial aggregation, that allows one to switch from a spatial entity to its components, and vice versa, and a relation of carto-graphic generalization, that allows one to maintain multiple spatial representations that may differ in resolution, shape, or point of view. A special attention is deserved to the problem of guaranteeing the topological consistency of different representations of the same spatial entity. A short description of the relational encoding of the constructs for multiple representations added to CGG concludes the paper.