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References
Abul O, Bonchi F, Nanni M (2008) Never walk alone: Uncertainty for anonymity in moving objects databases. In: 2008 IEEE 24th international conference on data engineering. IEEE, pp 376–385
Chen R, Fung BCM, Desai BC, Sossou NM (2012) Differentially private transit data publication: a case study on the montreal transportation system. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 213–221
Cheng R, Kalashnikov DV, Prabhakar S (2004) Querying imprecise data in moving object environments. IEEE Trans Knowl Data Eng 16(9):1112–1127
De Montjoye Y-A, Hidalgo CA, Verleysen M, Blondel VD (2013) Unique in the crowd: The privacy bounds of human mobility. Scientific Reports 3:1376
Deng K, Xie K, Zheng K, Zhou X (2011) Trajectory indexing and retrieval. Comput Spat Trajectories, 35–60
DiDi (2019) Internet+ signal control white paper
Draxler RR, Rolph GD (2003) Hysplit (hybrid single-particle lagrangian integrated trajectory). noaa air resources laboratory, silver spring, md. model access via noaa arl ready website
Guo C, Yang B, Hu J, Jensen C (2018) Learning to route with sparse trajectory sets. In: 2018 IEEE 34th international conference on data engineering (ICDE), pp 1073–1084. IEEE
He X, Cormode G, Machanavajjhala A, Procopiuc CM, Srivastava D (2015) Dpt: differentially private trajectory synthesis using hierarchical reference systems. Proc VLDB Endow 8(11):1154–1165
Koide S, Tadokoro Y, Xiao C, Ishikawa Y (2017) Cinct: Compression and retrieval for massive vehicular trajectories via relative movement labeling. Preprint. arXiv:1706.02885
Lee J-G, Han J, Whang K-Y (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, pp 593–604. ACM
Li Z, Ding B, Han J, Kays R (2010a) Swarm: Mining relaxed temporal moving object clusters. Proc VLDB Endow 3(1-2):723–734
Li Z, Ding B, Han J, Kays R, Nye P (2010b) Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1099–1108. ACM
Ruan S, Long C, Bao J, Li C, Yu Z, Li R, Liang Y, He T, Zheng Y (2020) Learning to generate maps from trajectories. AAAI
Song R, Sun W, Zheng B, Zheng Y (2014) Press: A novel framework of trajectory compression in road networks. Proc VLDB Endow 7(9):661–672
Su H, Zheng K, Wang H, Huang J, Zhou X (2013) Calibrating trajectory data for similarity-based analysis. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp 833–844. ACM
Tao Y, Papadias D (2001) The mv3r-tree: A spatio-temporal access method for timestamp and interval queries. In: Proceedings of very large data bases conference (VLDB), 11–14 September, Rome
Terrovitis M, Poulis G, Mamoulis N, Skiadopoulos S (2017) Local suppression and splitting techniques for privacy preserving publication of trajectories. IEEE Trans Knowl Data Eng 29(7):1466–1479
Wang H, Su H, Zheng K, Sadiq S, Zhou X (2013) An effectiveness study on trajectory similarity measures. In: Proceedings of the twenty-fourth Australasian database conference-volume 137, pp 13–22. Australian Computer Society
Xue C (2019) Space down to 1/7, an application of tdengine on china mobile iot’s trajectory data storage
Yang B, Guo C, Jensen CS (2013) Travel cost inference from sparse, spatio temporally correlated time series using markov models. Proc VLDB Endow 6(9):769–780
Yuan J, Zheng Y, Xie X, Sun G (2013) T-drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232
Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI conference on artificial intelligence
Zhao J, Xu J, Zhou R, Zhao P, Liu C, Zhu F (2018) On prediction of user destination by sub-trajectory understanding: A deep learning based approach. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1413–1422
Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transport Syst
Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol (TIST) 6(3):29
Zheng Y, Xie X (2011) Learning travel recommendations from user-generated gps traces. ACM Trans Intell Syst Technol (TIST) 2(1):2
Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer Science & Business Media
Zheng K, Zheng Y, Xie X, Zhou X (2012) Reducing uncertainty of low-sampling-rate trajectories. In: 2012 IEEE 28th international conference on data engineering (ICDE), pp 1144–1155. IEEE
Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2014) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Data Eng 26(8):1974–1988
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Zhou, X., Li, L., He, D. (2022). Spatio-Temporal Data - From Trajectory Management to Mining. In: Zomaya, A., Taheri, J., Sakr, S. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_221-2
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DOI: https://doi.org/10.1007/978-3-319-63962-8_221-2
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Spatio-Temporal Data - From Trajectory Management to Mining- Published:
- 25 February 2022
DOI: https://doi.org/10.1007/978-3-319-63962-8_221-2
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Spatiotemporal Data: Trajectories- Published:
- 01 February 2018
DOI: https://doi.org/10.1007/978-3-319-63962-8_221-1