Hierarchical graph-based approaches provide valuable abstractions for studying emerging topological structures and favour a better understanding of mobility patterns and similarities. This research introduces a hierarchical similarity measure that applies a series of distances toward a hierarchical graph-based model. This similarity measure is derived from different categories of distances and is experimented with evolving hierarchical graphs extracted from a European maritime mobility dataset.

Hierarchical graph-based approaches provide valuable abstractions for studying the topological structures that emerge and a better understanding of mobility patterns at complementary spatial and temporal scales. This research introduces a hierarchical graph-based model to represent and analyse a maritime transportation network at several spatio-temporal and semantic levels, as well as a new hierarchical extension of an accessibility measure. The proposed model is implemented in a graph database using maritime mobility data. The peculiarities of the hierarchical accessibility are computationally evaluated in comparison with non-hierarchical approaches.

Exploiting semantic information related to human mobility is particularly useful in crowd-sourcing environments where multidimensional data represent human trajectories and contextual information arising in indoor and outdoor spaces. This paper introduces a modelling approach and data manipulation mechanisms that represent semantic trajectories at different levels of abstraction. The objective is to produce a hybrid spatial representation for continuous mobility patterns emerging in indoor and outdoor spaces. This approach is based on a multi-layered graph that represents trajectories derived on the fly according to some given spatio-temporal constraints. The multi-layer graph provides a hierarchy of semantic places that offers several data manipulation capabilities according to given contextual and user-defined criteria.

Modern and connected mobilities have generated an explosive growth of location-based information. Such location data together with related crowd-sensed information, are noticeably available for humans navigating in both indoor and outdoor spaces. Considering the diversity of such multi-environment spaces, and where mobility occurs, raises several data modelling, management and processing research challenges. This not only implies to develop appropriate database architecture for large streamed data but also to identify the most appropriate data abstractions to model these human trajectories at the semantic level. While recent approaches often considered this issue using common stops and moves model, this does not completely cover the multi-dimensional contextual information that arises for humans navigating through indoor and outdoor spaces. This paper introduces a model of semantic trajectories evolving in both indoor and outdoor spaces, and cross-related with contextual information. This model defines a semantic trajectory considering multiple collaborative data semantics at different abstraction levels, and where trajectory segmentation relies on evolving semantic values. This enables (i) a unified indoor and outdoor spatial representation for trajectory annotation and (ii) multidimensional data integration and management. The final aim is to support semantic querying for a better understanding of human mobilities in urban environments.

S.192-197

Continuous location-dependent queries can be considered as key elements for the development of different categories of location-based and context-aware services. However, most work on location-dependent query processing has been mainly oriented towards outdoor environments. This paper studies location-dependent and context-aware queries over moving objects in indoor environments (e.g., houses, commercial malls, etc.), with a special focus on navigation-related queries (i.e., mainly path search and range queries). A hierarchical and context-dependent spatial data model is firstly presented, which leads to the consideration of other contextual dimensions besides the location of the involved entities, such as time and user profiles. Two algorithms for continuous processing of path and range queries on top of this modelling approach are introduced. The former performs a hierarchical and incremental path search for the continuous processing of path queries, and applied to both static and moving objects. The latter presents an incremental approach for continuous range queries, which performs a hierarchical network expansion around the initial query point and implements a mechanism to update the initial search tree based on user's movements.