Abstract
The headway of overall arranging development, sensor systems and versatile terminal, an extensive number obviously information are amassed. Bearing information contains an abundance of information, including directionality, time game-plan, and other outside expressive qualities. The examination obviously likeness estimation is the prelude of heading information the board and excavation, which acknowledge a fundamental occupation in bearing getting ready. Most course likeness work just spotlights on the dimensional-normal highlights. The augmentation of multi-credits to the heading changes the course furtiveness. MELD (Most extraordinary Least Direction Separation) and TLDS (Total of least Direction Separation) and inspect the association among the direction-common furtiveness and scholarly similarity. The headings including the zones, accurate location, and obvious characters are called multi-qualities bearings.
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References
Alokwatve, T.: Topological transformation approaches to database query processing. In: ACM SIGMOD Conference, vol. 9, no. 3, pp. 18–25 (2017)
Chen, K., Guo, S., Kavuluru, R.: ACM Data and Application, vol. 3, pp. 18–25 (2011)
Chen, K., Liu, L.: Geometric data perturbation for outsourced data mining. Knowl. Inf. Syst. 5(6), 965–981 (2012)
Xu, H., Liu, K., Mitchell, L., Sun, G.: Building confidential and efficient query services in the cloud with RASP data perturbation. In: SIAM Data Mining Conference, vol. 10, no. 3, pp. 18–25 (2017)
Zhu, H.S.R., Konwinski, A.: Range based neighbor queries with complex shaped obstacles. Technical Report, University of Berkeley (2015). vol. 12, no. 3, pp. 18–25 (2015)
Shen, H.J., Mitchell, J.C.: Leveraging a compound graph based DHT for multi attribute range queries with performance analysis. IEEE Secur. Privacy 9(3), 18–25 (2013)
Wen, M.I., Vandenberghe, L.: A PARQ-preserving range query scheme over encrypted metering data for smart grid, vol. 13, no. 3, pp. 18–25 (2016)
Qijun Zhu, M.K., Goldreich, O., Kushilevitz, E.: Querying distributed partial data sets with unknown region. ACM Comput. Surv. 45(6), 965–981 (2017)
Li, R.P.: Fast and scalable range query processing with strong privacy protection for cloud computing. In: INFOCOMMDC, vol. 4, no. 2, pp. 18–25 (2014)
Xin range Qijunreich, J., Mitchell, J.C.: Skyline queries in mobile environments. IEEE Secur. Priv. 9(3), 18–25 (2016)
Furtado, A.S., Kopanaki, D., Alvares, L.O., et al.: Multidimensional similarity measuring for semantic trajectories. Trans. GIS 20(2), 280–298 (2016)
Arboleda, F.J.M., Fernández, S.R., Bogorny, V.: Towards a semantic trajectory similarity measuring. Indian J. Sci. Technol. 10(18), 1–14 (2017)
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Monica Rachel, K., Joy Winnie Wise, D.C., Raja Sundari, K., Raja Priya, N. (2020). Data Analysis in Social Networks Based on Similarity Measurements on Multi-attribute Trajectories. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_53
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