“AccessBIM” - A Model of Environmental Characteristics for Vision Impaired Indoor Navigation and Way Finding

The complexity of modern indoor environments has made navigation difficult for individuals with vision impairment. Hence, this thesis presents the AccessBIM framework, which is an optimized database that’s facilitates generation of a real-time floor plan with path determination. The AccessBIM framework has the potential to play an integral role in improving the independence and quality of life for people with vision impairment whilst also decreasing the cost to the community related to caretakers

Traitement continu des requêtes dépendantes de la localisation dans des environnements intérieurs

Cet article développe une représentation de données spatiales d’un environnement intérieur dit “indoor” qui tient compte des dimensions contextuelles centrées sur l’utilisateur et aborde les enjeux de gestion de données mobiles. Un modèle de données “indoor” hiérarchique et sensible au contexte est proposé. Cette conception hiérarchique favorise un traitement adaptatif et efficace des requêtes dépendantes de la localisation. Un langage de requêtes continues est développé et illustré par des exemples de requêtes. Cette approche de modélisation est complétée par le développement d’algorithmes de traitement continu des requêtes de recherche de chemin hiérarchique et des requêtes de zone sur des objets mobiles en “indoor”. Une étude expérimentale des solutions développées a été menée pour évaluer la performance et le passage à l’échelle à l’égard des propriétés intrinsèques des solutions proposées

Data Models for Moving Objects in Road Networks – Implementation and Experiences

Paper deals with the specific LBS scenario – Fleet management (FM) and more specifically with systems for Automatic vehicle location (AVL). Well designed and implemented spatial data model for moving objects is one of the most significant elements of any AVL system. In practical applications the results of the latest scientific research are seldom applied, despite the fact that this area has been developing intensively for more than 20 years. The reasons for this are analysed in the paper. Short analysis of functionality of these systems is presented considering the impact of these functionalities on the implemented data model for moving objects and more specifically their impact on spatio-temporal component of the model. The paper especially reviews the possibility of using road networks as a basis for the representation of moving objects data models and a fact that these models are rarely used in practical applications. A solution overcoming this situation is proposed. The solution assumes transition from the system that is not based on road network to the system that is based on network. There are quite few research papers dealing with OSM data models. Therefore, a significant space in this paper is dedicated to the description of these models since OSM data can be valuable for this type of applications

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements