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
Citation
GUO, Xiaonan; CHAN, Eddie C. L.; LIU, Ce; WU, Kaishun; LIU, Siyuan; and NI, Lionel.
ShopProfiler: Profiling Shops with Crowdsourcing Data. (2014). INFOCOM, 2014 Proceedings IEEE. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3478
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.