Publication Type

Conference Proceeding Article

Publication Date

5-2014

Abstract

Sensing data from mobile phones provide us exciting and profitable applications. Recent research focuses on sensing indoor environment, but suffers from inaccuracy because of the limited reachability of human traces or requires human intervention to perform sophisticated tasks. In this paper, we present ShopProfiler, a shop profiling system on crowdsourcing data. First, we extract customer movement patterns from traces. Second, we improve accuracy of building floor plan by adopting a gradient-based approach and then localize shops through WiFi heat map. Third, we categorize shops by designing an SVM classifier in shop space to support multi-label classification. Finally, we infer brand name from SSID by applying string similarity measurement. Based on over five thousand traces in three big malls in two different countries, we conclude that ShopProfiler achieves better accuracy in building refined floor plan, and characterizes shops in terms of location, category and name with little human intervention.

Keywords

Legged locomotion, Mobile handsets, IEEE 802.11 Standards, Sociology, Statistics, Radiation detectors

Discipline

Computer Sciences | Databases and Information Systems

Publication

INFOCOM, 2014 Proceedings IEEE

ISBN

978-1-4799-3360-0

Identifier

10.1109/INFOCOM.2014.6848056

Publisher

IEEE

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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