Towards a unified model of outdoor and indoor spaces
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Reasoning about RFID-tracked moving objects in symbolic indoor spaces
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Spatial keyword querying of geo-tagged web content
Proceedings of the 7th International Workshop on Ranking in Databases
A graph model for false negative handling in indoor RFID tracking data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
UniModeling: a tool for the unified modeling and reasoning in outdoor and indoor spaces
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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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.