A foundation for efficient indoor distance-aware query processing

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...

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Bibliographic Details
Main Authors: Jensen, Christian S., Lu, Hua, Cao, Xin
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/99496
http://hdl.handle.net/10220/12982
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.