Finding Frequently Visited Indoor POIs Using Symbolic Indoor Tracking Data

Hua Lu, Chenjuan Guo, Bin Yang, Christian S. Jensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Indoor tracking data is being amassed due to the deploymentof indoor positioning technologies. Analysing such data dis-closes useful insights that are otherwise hard to obtain. Forexample, by studying tracking data from an airport, we canidentify the shops and restaurants that are most popularamong passengers. In this paper, we study two query typesfor finding frequently visited Points of Interest (POIs) fromsymbolic indoor tracking data. The snapshot query findsthose POIs that were most frequently visited at a giventime point, whereas the interval query finds such POIs fora given time interval. A typical example of symbolic track-ing is RFID-based tracking, where an object with an RFIDtag is detected by an RFID reader when the object is in thereader’s detection range. A symbolic indoor tracking systemdeploys a limited number of proximity detection devices, likeRFID readers, at preselected locations, covering only part ofthe host indoor space. Consequently, symbolic tracking datais inherently uncertain and only enables the discrete cap-ture of the trajectories of indoor moving objects in terms ofcoarse regions. We provide uncertainty analyses of the datain relation to the two kinds of queries. The outcomes of theanalyses enable us to design processing algorithms for bothquery types. An experimental evaluation with both real andsynthetic data suggests that the framework and algorithmsenable efficient and scalable query processing.
Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2016
EditorsEvaggelia Pitoura, Sofian Maabout, Georgia Koutrika, Amélie Marian, Letizia Tanca, Ioana Manolescu, Kostas Stefanidis
Number of pages12
PublisherOpenProceedings.org
Publication date2016
Pages449-460
ISBN (Print)9783893180707
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event19th International Conference on Extending Database Technology - Bordeaux, France
Duration: 15 Mar 201618 Mar 2016
Conference number: 19
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=47855

Conference

Conference19th International Conference on Extending Database Technology
Number19
CountryFrance
CityBordeaux
Period15/03/201618/03/2016
Internet address

Cite this

Lu, H., Guo, C., Yang, B., & Jensen, C. S. (2016). Finding Frequently Visited Indoor POIs Using Symbolic Indoor Tracking Data. In E. Pitoura, S. Maabout, G. Koutrika, A. Marian, L. Tanca, I. Manolescu, & K. Stefanidis (Eds.), Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2016 (pp. 449-460). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2016.41