Identifying Typical Movements Among Indoor Objects - Concepts and Empirical Study

Laura Radaelli, Dovydas Sabonis, Hua Lu, Christian Søndergaard Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

8 Citations (Scopus)

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 languageEnglish
Title of host publication2013 IEEE 14th International Conference on Mobile Data Management
PublisherIEEE Computer Society Press
Publication date2013
Pages197-206
ISBN (Print)978-1-4673-6068-5
DOIs
Publication statusPublished - 2013
Eventthe 14th IEEE International Conference on Mobile Data Management - Milan, Italy
Duration: 3 Jun 20136 Jun 2013
Conference number: 14

Conference

Conferencethe 14th IEEE International Conference on Mobile Data Management
Number14
CountryItaly
CityMilan
Period03/06/201306/06/2013
SeriesI E E E International Conference on Mobile Data Management. Proceedings
ISSN1551-6245

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