Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. In this paper, we describe an experiment that held within the museum of natural history of La Rochelle with a class of high school volunteers. We will explain our systems that has been built to work in this specific case, and among other things formalize our algorithm for indoor localization that has not had an equivalent in the state of the art. The minimal zone searching algorithm (MZS) can compute in real time the position of a visitor, shaped as a zone with an average surface of 3 m${}^2$ when resources are limited and when the placement of nodes must respect the constraints imposed by the room’s layout. This method offered good results with data collected during the experimentation, such as a meaningful representation of the position of a visitor and most importantly a stable execution during the whole experience even when the subject was in tight spaces.

Mise en oeuvre d'une mémoire environnementale adaptée aux besoins d'un observatoire du domaine côtier : MEnIr Implementation of an environmental memory designed for a coastal domain observatory: MEnIr

Nowadays, smartphones and wearable devices are endowed with several sensors, which can harvest large quantities of data about urban areas (location information, pollution levels, etc.), going through a list of personal and surrounding contexts such as noise level, traffic awareness, to name a few. Exploiting this wealth of information provided by the crowd allows developers to design and build several applications over the so-called Mobile CrowdSensing, such as traffic regulation, environmental monitoring, tourism recommendation, etc. However, for this to be possible, many barriers still have to be overcome such as collecting, handling, structuring and representing the crowd data in a suitable way. In this paper, we propose a three-fold solution to the problem of data management in CrowdSensing systems. Firstly, we structure the collected data based on semantic ontologies. Secondly, we enrich the data based on a novel contextual awareness data interlinking. Finally, we refine recommendations with contexts, through taking into consideration meta-information interlinked to the main information of interest. We have implemented our model in a tourism recommendation application as a proof of concept. The experimental evaluation - which we carried out - has shown very promising results.

With the surge of mobile devices and applications, knowledge base browsing and querying facilities must be adapted to new kinds of users and services. Space and time databases are concerned and as such are the subject of this article. Within a mobile context, data retrieval services must take the location of the call into consideration as regards space and time, insofar many pieces of information do depend on ‘where’ and ‘when’ constraints. We present here with a general object model dedicated to specifying time properties. The novel aspects of our proposal consist of three main points. First, the temporal object model stands as a pivot model, and all the more leverages interoperability between software applications, since it extends common standards, such as ISO 19108 ISO, 2002. Text of 19108 Geographic information – Temporal schema [Google Scholar] and iCalendar. Second, we accompany the object model with a formal grammar close to natural languages, which helps end users in managing and checking temporal object instances. Finally, our proposal focuses on intensional temporal expressions instead of extensional sets of concrete calendar dates. This allows to express semantic aspects – namely for periodic (cyclic) events – which remain hidden and not computable when only a series of dates are given. We evoke two kinds of applications for which our model is beneficial; one concerns services called by human users and the other addresses calls placed by software agents e.g. in a multi-agent system for instance within a simulation context.