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Agarwal PK, Arge L, Erickson J. Indexing moving points. In: Proceedings of the 19th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2000. p. 175–86.
Basch J, Guibas LJ, Hershberger J. Data structures for mobile data. In: Proceedings of the 8th Annual ACM-SIAM Symposium on Discrete Algorithms; 1997. p. 747–56.
Chen S, Jensen CS, Lin D. A benchmark for evaluating moving object indexes. Proc VLDB Endowment 2008;1(2):1574–85.
Jensen CS, Lin D, Ooi BC. Query and update efficient B+-tree based indexing of moving objects. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 768–79.
Kollios G, Gunopulos D, Tsotras VJ. On indexing mobile objects. In: Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 1999. p. 261–72.
Mokbel MF, Ghanem TM, Aref WG. Spatio-temporal access methods. IEEE Data Eng Bull. 2003;26(2):40–9.
Patel JM, Chen Y, Chakka VP. STRIPES: an efficient index for predicted trajectories. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2004. p. 637–46.
Prabhakar S, Xia Y, Kalashnikov DV, Aref WG, Hambrusch SE. Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects. IEEE Trans Comput. 2002;51(10):1124–40.
Procopiuc CM, Agarwal PK, Har-Peled S. STAR-tree: an efficient self-adjusting index for moving objects. In: Proceedings of the 4th Workshop on Algorithm Engineering and Experiments; 2002. p. 178–93.
Šaltenis S, Jensen CS. Indexing of moving objects for location-based services. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 463–72.
Šaltenis S, Jensen CS, Leutenegger ST, Lopez MA. Indexing the positions of continuously moving objects. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 331–42.
Tao Y, Papadias D, Sun J. The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003. p. 790–801.
Tao Y, Xiao X. Primal or dual: which promises faster spatiotemporal search? VLDB J. 2008;17(5): 1253–70.
Tayeb J, Ulusoy Ö, Wolfson O. A quadtree based dynamic attribute indexing method. Computer J. 1998;41(3):185–200.
Yiu ML, Tao Y, Mamoulis N. The Bdual-tree: indexing moving objects by space filling curves in the dual space. VLDB J. 2006;17(3):379–400.
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Šaltenis, S., Jensen, C.S. (2018). Indexing of the Current and Near-Future Positions of Moving Objects. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_201
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