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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sg-ntu-dr.10356-994962020-05-28T07:17:45Z A foundation for efficient indoor distance-aware query processing Jensen, Christian S. Lu, Hua Cao, Xin School of Computer Engineering IEEE International Conference on Data Engineering (28th : 2012 : Washington, D. C., US) DRNTU::Engineering::Computer science and engineering 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. 2013-08-05T03:29:15Z 2019-12-06T20:08:05Z 2013-08-05T03:29:15Z 2019-12-06T20:08:05Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99496 http://hdl.handle.net/10220/12982 10.1109/ICDE.2012.44 en |
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DRNTU::Engineering::Computer science and engineering Jensen, Christian S. Lu, Hua Cao, Xin A foundation for efficient indoor distance-aware query processing |
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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. |
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School of Computer Engineering |
author_facet |
School of Computer Engineering Jensen, Christian S. Lu, Hua Cao, Xin |
format |
Conference or Workshop Item |
author |
Jensen, Christian S. Lu, Hua Cao, Xin |
author_sort |
Jensen, Christian S. |
title |
A foundation for efficient indoor distance-aware query processing |
title_short |
A foundation for efficient indoor distance-aware query processing |
title_full |
A foundation for efficient indoor distance-aware query processing |
title_fullStr |
A foundation for efficient indoor distance-aware query processing |
title_full_unstemmed |
A foundation for efficient indoor distance-aware query processing |
title_sort |
foundation for efficient indoor distance-aware query processing |
publishDate |
2013 |
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https://hdl.handle.net/10356/99496 http://hdl.handle.net/10220/12982 |
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