Finding Influential Location via User Mobility and Trajectory

Author:

Adu-Gyamfi DanielORCID,Zhang FengliORCID,Zhou FanORCID

Publisher

Springer Singapore

Reference20 articles.

1. Nai CC, Wanqin X, Jenny X (2017) Comprehensive predictions of tourists’ next visit location based on call detail records using machine learning and deep learning methods. In: IEEE 6th international congress on big data

2. Hussain MM, Trajcevski G, Islam KA, Ali ME (2017) Towards efficient maintenance of continuous MaxRS query for trajectories. In: 20th PEDBT, OpenProceedings.org, pp 21–24

3. Amagata D, Hara T (2017) A general framework for MaxRS and MaxCRS monitoring in spatial data streams. ACM Trans Spatial Algo Syst (TSAS) 3(1):1–34

4. Michael M, Maguelonne T (2006) Mining spatio-temporal data. J Intell Inf Syst 27:187–190. (Springer, LLC). https://doi.org/10.1007/s10844-006-9949-3

5. Renso C, Spaccapietra S, Zimanyi E (2013) Mobility data: modeling, management, and understanding. Cambridge Univ. Press, Edited. ISBN 978-1-107-02171-6

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1. Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients;Journal of Information Technology Research;2022-01

2. EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient;BMC Medical Informatics and Decision Making;2020-09-29

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