A foundation for efficient indoor distance-aware query processing

Abstract

Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and moving populations. A variety of services, e.g., location-based services and security control, are relevant to indoor spaces. Such services can be improved substantially if they are capable of utilizing indoor distances. However, existing indoor space models do not account well for indoor distances. To address this shortcoming, we propose a data management infrastructure that captures indoor distance and facilitates distance-aware query processing. In particular, we propose a distance-aware indoor space model that integrates indoor distance seamlessly. To enable the use of the model as a foundation for query processing, we develop accompanying, efficient algorithms that compute indoor distances for different indoor entities like doors as well as locations. We also propose an indexing framework that accommodates indoor distances that are pre-computed using the proposed algorithms. On top of this foundation, we develop efficient algorithms for typical indoor, distance-aware queries. The results of an extensive experimental evaluation demonstrate the efficacy of the proposals.