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王撷阳 等: 基于数据分布的移动对象学习索引及查询算法                                                     2233


                 [33]   Tang CZ, Wang YY, Dong ZY, Hu GS, Wang ZG, Wang MJ, Chen HB. XIndex: A scalable learned index for multicore data storage. In:
                     Proc. of the 25th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming. San Diego: ACM, 2020. 308–320. [doi: 10.
                     1145/3332466.3374547]
                 [34]   Wang YY, Tang CZ, Wang ZG, Chen HB. SIndex: A scalable learned index for string keys. In: Proc. of the 11th ACM SIGOPS Asia-
                     Pacific Workshop on Systems. Tsukuba: ACM, 2020. 17–24. [doi: 10.1145/3409963.3410496]
                 [35]   Ding JL, Minhas UF, Yu J, Wang C, Do J, Li YN, Zhang HT, Chandramouli B, Gehrke J, Kossmann D, Lomet DB, Kraska T. ALEX: An
                     updatable  adaptive  learned  index.  In:  Proc.  of  the  2020  ACM  SIGMOD  Int’l  Conf.  on  Management  of  Data.  Portland:  ACM,  2020.
                     969–984. [doi: 10.1145/3318464.3389711]
                 [36]   Ferragina P, Vinciguerra G. The PGM-index: A fully-dynamic compressed learned index with provable worst-case bounds. Proc. of the
                     VLDB Endowment, 2020, 13(8): 1162–1175. [doi: 10.14778/3389133.3389135]
                 [37]   Marcus R, Kipf A, van Renen A, Stoian M, Misra S, Kemper A, Neumann T, Kraska T. Benchmarking learned indexes. Proc. of the
                     VLDB Endowment, 2020, 14(1): 1–13. [doi: 10.14778/3421424.3421425]
                 [38]   Zhang JY, Gao YH. CARMI: A cache-aware learned index with a cost-based construction algorithm. Proc. of the VLDB Endowment,
                     2022, 15(11): 2679–2691. [doi: 10.14778/3551793.3551823]
                 [39]   Wu JC, Zhang Y, Chen SM, Wang J, Chen Y, Xing CX. Updatable learned index with precise positions. Proc. of the VLDB Endowment,
                     2021, 14(8): 1276–1288. [doi: 10.14778/3457390.3457393]
                 [40]   Li PF, Lu H, Zhu R, Ding BL, Yang L, Pan G. DILI: A distribution-driven learned index. Proc. of the VLDB Endowment, 2023, 16(9):
                     2212–2224. [doi: 10.14778/3598581.3598593]
                 [41]   Wang HX, Fu XY, Xu JL, Lu H. Learned index for spatial queries. In: Proc. of the 20th IEEE Int’l Conf. on Mobile Data Management.
                     Hong Kong: IEEE, 2019. 569–574. [doi: 10.1109/MDM.2019.00121]
                 [42]   Davitkova A, Milchevski E, Michel S. The ML-Index: A multidimensional, learned index for point, range, and nearest-neighbor queries.
                     In: Proc. of the 23th Int’l Conf. on Extending Database Technology. Copenhagen: OpenProceedings.org, 2020. 463–473.
                 [43]   Wang  JN,  Chen  C,  Zheng  ZB,  Chen  LN,  Zhou  YR.  Predicting  high-dimensional  time  series  data  with  spatial,  temporal  and  global
                     information. Information Sciences, 2022, 607: 477–492. [doi: 10.1016/j.ins.2022.06.021]
                 [44]   Ding JL, Nathan V, Alizadeh M, Kraska T. Tsunami: A learned multi-dimensional index for correlated data and skewed workloads. Proc.
                     of the VLDB Endowment, 2020, 14(2): 74–86. [doi: 10.14778/3425879.3425880]
                 [45]   Zhang SN, Ray S, Lu RX, Zheng YD. SPRIG: A learned spatial index for range and kNN queries. In: Proc. of the 17th Int’l Symp. on
                     Spatial and Temporal Databases. Springer, 2021. 96–105. [doi: 10.1145/3469830.3470892]
                 [46]   Li PF, Lu H, Zheng Q, Yang L, Pan G. LISA: A learned index structure for spatial data. In: Proc. of the 2020 ACM SIGMOD Int’l Conf.
                     on Management of Data. Portland: ACM, 2020. 2119–2133. [doi: 10.1145/3318464.3389703]
                 [47]   Qi JZ, Liu GL, Jensen CS, Kulik L. Effectively learning spatial indices. Proc. of the VLDB Endowment, 2020, 13(12): 2341–2354. [doi:
                     10.14778/3407790.3407829]
                 [48]   Ding XF, Zheng YT, Wang Z, Choo KKR, Jin H. A learned spatial textual index for efficient keyword queries. Journal of Intelligent
                     Information Systems, 2023, 60(3): 803–827. [doi: 10.1007/s10844-022-00752-2]
                 [49]   Li JN, Wang Z, Cong G, Long C, Kiah HM, Cui B. Towards designing and learning piecewise space-filling curves. Proc. of the VLDB
                     Endowment, 2023, 16(9): 2158–2171. [doi: 10.14778/3598581.3598589]
                 [50]   Gao J, Cao X, Yao X, Zhang G, Wang W. LMSFC: A novel multidimensional index based on learned monotonic space filling curves.
                     Proc. of the VLDB Endowment, 2023, 16(10): 2605–2617. [doi: 10.14778/3603581.3603598]
                 [51]   Zhang Z, Jin PQ, Xie XK. Learned indexes: Current situations and research prospects. Ruan Jian Xue Bao/Journal of Software, 2021,
                     32(4): 1129–1150 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6168.htm [doi: 10.13328/j.cnki.jos.006168]
                 [52]   Galakatos A, Markovitch M, Binnig C, Fonseca R, Kraska T. FITing-Tree: A data-aware index structure. In: Proc. of the 2019 Int’l Conf.
                     on Management of Data. Amsterdam: ACM, 2019. 1189–1206. [doi: 10.1145/3299869.3319860]
                 [53]   Pai  S,  Mathioudakis  M,  Wang  YH.  WaZI:  A  learned  and  workload-aware  Z-index.  In:  Proc.  of  the  27th  Int’l  Conf.  on  Extending
                     Database Technology. Paestum: OpenProceedings.org, 2024. 559–571. [doi: 10.48786/EDBT.2024.48]
                 [54]   Yu  ZQ,  Xhafa  F,  Chen  YH,  Ma  K.  A  distributed  hybrid  index  for  processing  continuous  range  queries  over  moving  objects.  Soft
                     Computing, 2019, 23(9): 3191–3205. [doi: 10.1007/s00500-017-2973-0]
                 [55]   Baride S, Saxena AS, Goyal V. Efficiently mining colocation patterns for range query. Big Data Research, 2023, 31: 100369. [doi: 10.
                     1016/j.bdr.2023.100369]
                 [56]   Yi X, Paulet R, Bertino E, Varadharajan V. Practical approximate k nearest neighbor queries with location and query privacy. IEEE
                     Trans. on Knowledge and Data Engineering, 2016, 28(6): 1546–1559. [doi: 10.1109/TKDE.2016.2520473]
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