Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing

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• 作者：Tanvir Ahmed ; Torben Bach Pedersen ; Hua Lu
• 关键词：Indoor tracking ; Graph ; based model ; Dense location ; Spatio ; temporal indexing ; Interval query ; Aggregate query ; Duration query
• 刊名：GeoInformatica
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：21
• 期：1
• 页码：119-150
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Geographical Information Systems/Cartography; Data Structures, Cryptology and Information Theory; Computer Science, general; Information Storage and Retrieval; Multimedia Information Systems;
• 出版者：Springer US
• ISSN：1573-7624
• 卷排序：21

文摘

Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly.