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ISPRS Int. J. Geo-Inf. 2016, 5(10), 176; doi:10.3390/ijgi5100176

Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries

1,2,* , 1,2,* , 3
,
4
and
1,2
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, CAS Olympic S & T Park, No. 20 Datun Road, P.O. Box 9718, Beijing 100101, China
2
University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
3
Department of Computer Science, University of Massachusetts Boston, Boston, MA 02125, USA
4
Beijing Jinghang Computation and Communication Research Institute, Beijing 100074, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Sisi Zlatanova, Kourosh Khoshelham, George Sithole and Wolfgang Kainz
Received: 17 May 2016 / Revised: 8 September 2016 / Accepted: 18 September 2016 / Published: 27 September 2016
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)

Abstract

With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that—compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)—the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries. View Full-Text
Keywords: indoor adjacency matrix; indoor complex semantic query; semantic trajectory; indexing for indoor moving objects indoor adjacency matrix; indoor complex semantic query; semantic trajectory; indexing for indoor moving objects
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lin, H.; Peng, L.; Chen, S.; Liu, T.; Chi, T. Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries. ISPRS Int. J. Geo-Inf. 2016, 5, 176.

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