Abstract
With the proliferation of mobile computing, positioning systems are becoming available that enable indoor location-based services. As a result, indoor tracking data is also becoming available. This paper puts focus on one use of such data namely the identification of typical movement patterns among indoor moving objects. Specifically, the paper presents a method for the identification of movement patterns. Leveraging concepts from sequential pattern mining, the method takes into account the specifics of spatial movement and, in particular, the specifics of tracking data that captures indoor movement. For example, the paper’s proposal supports spatial aggregation and utilizes the topology of indoor spaces to achieve better performance. The paper reports on empirical studies with real and synthetic data that offer insights into the functional and computational aspects of its proposal.
Original language | English |
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Title of host publication | 2013 IEEE 14th International Conference on Mobile Data Management |
Publisher | IEEE Computer Society Press |
Publication date | 2013 |
Pages | 197-206 |
ISBN (Print) | 978-1-4673-6068-5 |
DOIs | |
Publication status | Published - 2013 |
Event | the 14th IEEE International Conference on Mobile Data Management - Milan, Italy Duration: 3 Jun 2013 → 6 Jun 2013 Conference number: 14 |
Conference
Conference | the 14th IEEE International Conference on Mobile Data Management |
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Number | 14 |
Country/Territory | Italy |
City | Milan |
Period | 03/06/2013 → 06/06/2013 |
Series | I E E E International Conference on Mobile Data Management. Proceedings |
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ISSN | 1551-6245 |