Cited By
View all- Jiang DDing YZhang HLiu YHe TYang YZhang D(2021)ALWAESProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34780815:3(1-24)Online publication date: 14-Sep-2021
POI Estimation Method with Incremental Update based on Smartphones
Pages 104 - 110
Abstract
As smartphones are widespread, it is very important in business to utilize effectively personal data generated by them. A lot of such data is inherently associated with certain locations or places. This paper proposes a POI(Point of Interest) estimation method with incremental update based on the data collected from smartphones.
References
[1]
UJIIndoorLoc Data Set, https://archive.ics.uci.edu/ml/datasets/ujiindoorloc.
[2]
G. Berkovich. Accurate and reliable real-time indoor positioning on commercial smartphones. In International conference on indoor positioning and indoor navigation, pages 27--30, 2014.
[3]
S. Y. Cho. Hybrid linear closed-form solution in wireless localization. ETRI Journal, 37(3):533--540, 2015.
[4]
T. M. T. Do and D. Gatica-Perez. The places of our lives: Visiting patterns and automatic labeling from longitudinal smartphone data. Mobile Computing, IEEE Transactions on, 13(3):638--648, 2014.
[5]
C. Gurrin, A. F. Smeaton, and A. R. Doherty. Lifelogging: Personal big data. Foundations and Trends in Information Retrieval, 8(1):1--125, 2014.
[6]
K. Kaemarungsi and P. Krishnamurthy. Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM 2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, volume 2, pages 1012--1022. IEEE, 2004.
[7]
N. Newman. Apple ibeacon technology briefing. Journal of Direct, Data and Digital Marketing Practice, 15(3):222--225, 2014.
[8]
S. K. Shivendra Tiwari. User category based estimation of location popularity using the road gps trajectory databases. Geoinformatica - An International Journal (GIIJ), 4(2):20--31, 2014.
[9]
S. Teerakanok, C. Vorakulpipat, S. Kamolphiwong, and S. Siwamogsatham. Preserving user anonymity in context-aware location-based services: a proposed framework. ETRI journal, 35(3):501--511, 2013.
[10]
J. Torres-Sospedra, R. Montoliu, A. Martınez-Usó, J. P. Avariento, T. J. Arnau, M. Benedito-Bordonau, and J. Huerta. Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems. In Proceedings of the fifth conference on indoor positioning and indoor navigation, 2014.
[11]
Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer. Semantic trajectories: Mobility data computation and annotation. ACM Transactions on Intelligent Systems and Technology (TIST), 4(3):49, 2013.
[12]
V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th international conference on World wide web, pages 1029--1038. ACM, 2010.
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Comments
Information & Contributors
Information
Published In
October 2015
321 pages
ISBN:9781450338462
DOI:10.1145/2837060
- Conference Chairs:
- Jongsup Choi,
- Sun Hwa Han,
- Joo-Yeoun Lee,
- Taeho Park,
- Editor:
- Aziz Nasridinov,
- Program Chairs:
- Carson K. Leung,
- Yoo-Sung Kim,
- Young-Koo Lee
Copyright © 2015 ACM.
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 October 2015
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- Research-article
- Research
- Refereed limited
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Conference
BigDAS '15
BigDAS '15: 2015 International Conference on Big Data Applications and Services
October 20 - 23, 2015
Jeju Island, Republic of Korea
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Cited By
View all- Jiang DDing YZhang HLiu YHe TYang YZhang D(2021)ALWAESProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34780815:3(1-24)Online publication date: 14-Sep-2021
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