时空数据查询技术研究综述

doi:10.3778/j.issn.1673-9418.2411033

摘要/Abstract

摘要： 随着现代信息技术的快速发展与应用，时空数据的规模迅速增长。这些数据呈现出海量聚集、高维异构以及动态复杂等特点。近年来，以时空数据为背景的时空查询技术得到广泛的研究和应用，如何有效地存储、管理和查询这些数据成为了研究的重点。对时空数据的相关查询技术进行综述，从时空数据相关基本概念入手，系统阐述了当前主流的时空查询处理模式，涵盖了范围查询、K近邻查询、反K近邻查询等多种类型；介绍了不同的时空索引技术，包括基于轨迹的索引结构、基于抽样的索引以及其他创新的索引方法；分析了结合其他技术的查询方法，主要包括时空-文本查询、语义近似轨迹查询、并行和分布式查询等，这些技术不仅提升了时空查询的多样性和准确性，还能有效地处理大规模时空数据。展望了时空查询技术的未来发展方向，包括查询结果的可视化展示、隐私保护以及结合机器学习的新型索引结构，为时空数据的高效利用提供了新的思路和挑战。

关键词: 时空数据, 查询处理, 索引技术, 时空-文本, 语义近似, 分布式

Abstract: With the rapid development and application of modern information technology, the scale of spatio-temporal data has grown rapidly. These data exhibit characteristics such as massive aggregation, high dimensionality, heterogeneity, and dynamic complexity. In recent years, spatio-temporal query technologies based on spatio-temporal data have been widely researched and applied, making the effective storage, management, and querying of these data a key focus of research. This paper provides an overview of relevant query technologies for spatio-temporal data, starting with the basic concepts related to spatio-temporal data, systematically explaining the current mainstream spatio-temporal query processing models, including various types such as range queries, K-nearest neighbor queries, and reverse K-nearest neighbor queries. Subsequently, different spatio-temporal indexing techniques are introduced, including trajectory-based indexing structures, sampling-based indexing, and other innovative indexing methods. At the same time, querying methods that integrate other technologies are analyzed, mainly including spatio-temporal textual queries, semantic approximate trajectory queries, and parallel and distributed queries. These technologies not only enhance the diversity and accuracy of spatio-temporal queries but also effectively handle large-scale spatio-temporal data. Finally, the future development directions of spatio-temporal query technologies are discussed, including the visualization of query results, privacy protection, and new indexing structures that integrate machine learning, providing new ideas and challenges for the efficient utilization of spatio-temporal data.

Key words: spatio-temporal data, query processing, indexing techniques, spatio-temporal textual, semantic approximation, distributed

孟祥福, 翁雪, 徐永杰. 时空数据查询技术研究综述[J]. 计算机科学与探索, 2025, 19(8): 2001-2023.

MENG Xiangfu, WENG Xue, XU Yongjie. Review of Research on Spatio-Temporal Data Query Technologies[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(8): 2001-2023.

参考文献

[1] MAHMOOD A R, PUNNI S, AREF W G. Spatio-temporal access methods: a survey (2010-2017)[J]. GeoInformatica,2019, 23(1): 1-36.
[2] KIM D, CáNOVAS-SEGURA B, CAMPOS M, et al. Visualization of spatial-temporal epidemiological data: a scoping review[J]. Technologies, 2024, 12(3): 31.
[3] YANG Y Y, JIN M, WEN H M, et al. A survey on diffusion models for time series and spatio-temporal data[EB/OL]. [2024-09-23]. https://arxiv.org/abs/2404.18886.
[4] COMER D. Ubiquitous B-tree[J]. ACM Computing Surveys,1979, 11(2): 121-137.
[5] GUTTMAN A. R-trees: a dynamic index structure for spatial searching[C]//Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data. New York: ACM, 1984: 47-57.
[6] LOMET D, SALZBERG B. Access methods for multiversion data[J]. ACM SIGMOD Record, 1989, 18(2): 315-324.
[7] ZHOU P F, ZHANG D H, SALZBERG B, et al. Close pair queries in moving object databases[C]//Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems. New York: ACM, 2005: 2-11.
[8] THEODORIDIS Y, VAZIRGIANNIS M, SELLIS T. Spatio-temporal indexing for large multimedia applications[C]//Proceedings of the 3rd IEEE International Conference on Multimedia Computing and Systems. Piscataway: IEEE, 2002: 441-448.
[9] ?ALTENIS S, JENSEN C S, LEUTENEGGER S T, et al. Indexing the positions of continuously moving objects[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000: 331-342.
[10] DITTRICH J, BLUNSCHI L, VAZ SALLES M A. Indexing moving objects using short-lived throwaway indexes[C]//Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer, 2009: 189-207.
[11] TROPF H, HERZOG H. Multidimensional range search in dynamically balanced trees[J]. Angewandte Informatik,1981(2): 71-77.
[12] ORENSTEIN J A, MERRETT T H. A class of data structures for associative searching[C]//Proceedings of the 3rd ACM SIGACT-SIGMOD Symposium on Principles of Database Systems. New York: ACM, 1984: 181.
[13] TAYEB J,ULUSOY ?,WOLFSON O. A quadtree-based dynamic attribute indexing method[J]. The Computer Journal,1998, 41(3): 185-200.
[14] CUDRE-MAUROUX P, WU E, MADDEN S. TrajStore: an adaptive storage system for very large trajectory data sets[C]//Proceedings of the 2010 IEEE 26th International Conference on Data Engineering. Piscataway: IEEE, 2010: 109-120.
[15] SAMET H. Applications of spatial data structures: computer graphics, image processing, and GIS[M]. Boston: Addison-Wesley Longman Publishing Co., Inc., 1990.
[16] PATEL J M, CHEN Y, CHAKKA V P. STRIPES: an efficient index for predicted trajectories[C]//Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2004: 635-646.
[17] KWON D, LEE S J, LEE S. Indexing the current positions of moving objects using the lazy update R-tree[C]//Proceedings of the 3rd International Conference on Mobile Data Management. Piscataway: IEEE, 2002: 113-120.
[18] CHAKKA V P, EVERSPAUGH A, PATEL J M. Indexing large trajectory data sets with SETI[C]//Proceedings of the 1st Biennial Conference on Innovative Data Systems Research, 2003: 76.
[19] CRESSIE N, WIKLE C K. Statistics for spatio-temporal data[M]. Hoboken: John Wiley & Sons, 2011.
[20] DITTRICH J, QUIANé-RUIZ J A. Efficient big data processing in hadoop MapReduce[J]. Proceedings of the VLDB Endowment, 2012, 5(12): 2014-2015.
[21] AHARIA M, XIN R S, WENDELL P, et al. Apache Spark: a unified engine for big data processing[J]. Communications of the ACM, 2016, 59(11): 56-65.
[22] GüTING R H, LU J. Parallel secondo: scalable query processing in the cloud for non-standard applications[J]. Sigspatial Special, 2015, 6(2): 3-10.
[23] ALARABI L, MOKBEL M F. A demonstration of ST-hadoop: a MapReduce framework for big spatio-temporal data[J]. Proceedings of the VLDB Endowment, 2017, 10(12): 1961-1964.
[24] ALARABI L. Summit: a scalable system for massive trajectory data management[C]//Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2018: 612-613.
[25] NIKITOPOULOS P, VLACHOU A, DOULKERIDIS C,et al. DiStRDF: distributed spatio-temporal RDF queries on spark[C]//Proceedings of the Workshops of the EDBT/ICDT 2018 Joint Conference, 2018: 125-132.
[26] SHANG Z Y, LI G L, BAO Z F. DITA: distributed in-memory trajectory analytics[C]//Proceedings of the 2018 International Conference on Management of Data. New York: ACM, 2018: 725-740.
[27] FANG Z Q, CHEN L, GAO Y J, et al. Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics[J]. The VLDB Journal, 2021, 30(2): 287-310.
[28] NISHIMURA S, DAS S, AGRAWAL D, et al. MD-HBase: a scalable multi-dimensional data infrastructure for location aware services[C]//Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management. Piscataway: IEEE, 2011: 7-16.
[29] NIDZWETZKI J K, GüTING R H. Distributed SECONDO: a highly available and scalable system for spatial data processing[C]//Advances in Spatial and Temporal Databases. Cham: Springer, 2015: 491-496.
[30] QIN J W, MA L L, NIU J H. THBase: a coprocessor-based scheme for big trajectory data management[J]. Future Internet,2019, 11(1): 10.
[31] PFOSER D, JENSEN C S, THEODORIDIS Y. Novel approaches in query processing for moving object trajectories[C]//Proceedings of the 26th International Conference on Very Large Data Bases, 2000: 395-406.
[32] NASCIMENTO M A, SILVA J R O. Towards historical R-trees[C]//Proceedings of the 1998 ACM symposium on Applied Computing. New York: ACM, 1998: 235-240.
[33] JENSEN C, LIN D, OOI B. Query and update efficient B-tree based indexing of moving objects[C]//Proceedings of the 30th International Conference on Very Large Data Bases.San Francisco: Morgan Kaufmann, 2004: 768-779.
[34] ELBASSIONI K, ELMASRY A, KAMEL I. An efficient indexing scheme for multi-dimensional moving objects[C]//Proceedings of the 9th International Conference on Database Theory. Berlin, Heidelberg: Springer, 2003: 425-439.
[35] SAULYS D, JOHANSEN J M, CHRISTIANSEN C W. Indexing moving objects in main memory[C]//Proceedings of the 2008 Annual IEEE Student Paper Conference. Piscataway: IEEE, 2008: 1-5.
[36] KALASHNIKOV D, PRABHAKAR S, HAMBRUSCH S,et al. Efficient evaluation of continuous range queries on moving objects[C]//Proceedings of the 2002 International Conference on Database and Expert Systems Applications. Berlin, Heidelberg: Springer, 2002: 731-740.
[37] TAO Y F, PAPADIAS D. Time-parameterized queries in spatio-temporal databases[C]//Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2002: 334-345.
[38] TAO Y F, PAPADIAS D, SHEN Q M. Continuous nearest neighbor search[C]//Proceedings of the 28th International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann, 2002: 287-298.
[39] BENETIS R, JENSEN C S, KARCIAUSKAS G, et al. Nearest neighbor and reverse nearest neighbor queries for moving objects[C]//Proceedings of the 2002 International Database Engineering and Applications Symposium. Piscataway: IEEE, 2002: 44-53.
[40] BENETIS R, JENSEN C S, KAR?IAUSKAS G, et al. Nearest and reverse nearest neighbor queries for moving objects[J]. The VLDB Journal, 2006, 15(3): 229-249.
[41] LEE K C K, LEONG H V, ZHOU J, et al. An efficient algorithm for predictive continuous nearest neighbor query processing and result maintenance[C]//Proceedings of the 6th International Conference on Mobile Data Management. New York: ACM, 2005: 178-182.
[42] RAPTOPOULOU K, PAPADOPOULOS A N, MANOLOPOULOS Y. Fast nearest-neighbor query processing in moving-object databases[J]. GeoInformatica, 2003, 7(2): 113-137.
[43] IWERKS G S, SAMET H, SMITH K. Continuous K-nearest neighbor queries for continuously moving points with updates[C]//Proceedings of the 29th International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann, 2003: 512-523.
[44] DONG T Y, YUAN L L, SHANG Y H, et al. Direction-aware continuous moving K-nearest-neighbor query in road networks[J]. ISPRS International Journal of Geo-Information, 2019, 8(9): 379.
[45] SONG Z X, ROUSSOPOULOS N. K-nearest neighbor search for moving query point[C]//Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer, 2001: 79-96.
[46] MOKBEL M F, XIONG X P, AREF W G. SINA: scalable incremental processing of continuous queries in spatio-temporal databases[C]//Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, 2004: 623-634.
[47] XIONG X, MOKBEL M F, AREF W G. SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases[C]//Proceedings of the 21st International Conference on Data Engineering. Piscataway: IEEE, 2005: 643-654.
[48] NEHME R V, RUNDENSTEINER E A. SCUBA: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects[C]//Proceedings of the 10th International Conference on Extending Database Technology. Berlin, Heidelberg: Springer, 2006: 1001-1019.
[49] HUANG Y K, CHEN C C, LEE C A. Continuous K-nearest neighbor query for moving objects with uncertain velocity[J]. GeoInformatica, 2009, 13(1): 1-25.
[50] HUANG Y K, LEE C A. Efficient evaluation of continuous spatio-temporal queries on moving objects with uncertain velocity[J]. GeoInformatica, 2010, 14(2): 163-200.
[51] HSUEH Y L, ZIMMERMANN R, KU W S. Efficient location updates for continuous queries over moving objects[J]. Journal of Computer Science and Technology, 2010, 25(3): 415-430.
[52] MOURATIDIS K, YIU M L, PAPADIAS D, et al. Continuous nearest neighbor monitoring in road networks[C]//Proceedings of the 32nd International Conference on Very Large Data Bases. New York: ACM, 2006: 43-54.
[53] WANG H J, ZIMMERMANN R. Processing of continuous location-based range queries on moving objects in road networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(7): 1065-1078.
[54] FAN P, LI G H, YUAN L, et al. Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks[J]. Information Systems, 2012, 37(1): 13-32.
[55] SUN H L, JIANG C, LIU J L, et al. Continuous reverse nearest neighbor queries on moving objects in road networks[C]//Proceedings of the 9th International Conference on Web-Age Information Management. Piscataway: IEEE, 2008: 238-245.
[56] NELSON R C, SAMET H. A consistent hierarchical representation for vector data[C]//Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM, 1986: 197-206.
[57] XU C F, GU Y, CHEN L, et al. Interval reverse nearest neighbor queries on uncertain data with Markov correlations[C]//Proceedings of the 2013 IEEE 29th International Conference on Data Engineering. Piscataway: IEEE, 2013: 170-181.
[58] EMRICH T, KRIEGEL H P, MAMOULIS N, et al. Reverse-nearest neighbor queries on uncertain moving object trajectories[C]//Proceedings of the 19th International Conference on Database Systems for Advanced Applications. Cham: Springer, 2014: 92-107.
[59] SUN J, PAPADIAS D, TAO Y F, et al. Querying about the past, the present, and the future in spatio-temporal databases[C]//Proceedings of the 20th International Conference on Data Engineering. Piscataway: IEEE, 2004: 202-213.
[60] SHEN Z. Efficient processing of Top-k queries on spatial and temporal data[D]. Sydney: University of New South Wales, 2012.
[61] CHEN S, OOI B C, TAN K L, et al. ST2B-tree: a self-tunable spatio-temporal B+-tree index for moving objects[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2008: 29-42.
[62] YIU M L, TAO Y F, MAMOULIS N. The Bdual-Tree: indexing moving objects by space filling curves in the dual space[J]. The VLDB Journal, 2008, 17(3): 379-400.
[63] BECKMANN N, KRIEGEL H P, SCHNEIDER R, et al. The R*-tree: an efficient and robust access method for points and rectangles[J]. ACM SIGMOD Record, 1990, 19(2): 322-331.
[64] TAO Y F, PAPADIAS D, SUN J M. The TPR*-tree an optimized spatio-temporal access method for predictive queries[C]//Proceedings of 29th International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann, 2003: 790-801.
[65] SALTENIS S, JENSEN C S. Indexing of moving objects for location-based services[C]//Proceedings of the 18th International Conference on Data Engineering. Piscataway: IEEE, 2002: 463-472.
[66] TAO Y F, FALOUTSOS C, PAPADIAS D, et al. Prediction and indexing of moving objects with unknown motion patterns[C]//Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2004: 611-622.
[67] FANG Y, CAO J H, WANG J Z, et al. HTPR*-tree: an efficient index for moving objects to support predictive query and partial history query[C]//Proceedings of the 2012 International Workshops on Web-Age Information Management. Berlin, Heidelberg: Springer, 2012: 26-39.
[68] SAMET H. The quadtree and related hierarchical data structures[J]. ACM Computing Surveys, 1984, 16(2): 187-260.
[69] SONG Z X, ROUSSOPOULOS N. Hashing moving objects[C]//Proceedings of the 2001 International Conference on Mobile Data Management. Berlin, Heidelberg: Springer, 2001: 161-172.
[70] SONG Z X, ROUSSOPOULOS N. SEB-tree: an approach to index continuously moving objects[C]//Proceedings of the 2002 International Conference on Mobile Data Management. Berlin, Heidelberg: Springer, 2002: 340-344.
[71] BURTON F W, KOLLIAS J G, MATSAKIS D G, et al. Short note: implementation of overlapping B-trees for time and space efficient representation of collections of similar files[J]. The Computer Journal, 1990, 33(3): 279-280.
[72] TAO Y, PAPADIAS D. The mv3r-tree: a spatio-temporal access method for timestamp and interval queries[C]//Proceedings of the 27th International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann, 2001: 431-440.
[73] BECKER B, GSCHWIND S, OHLER T, et al. An asymptotically optimal multiversion B-tree[J]. The VLDB Journal, 1996, 5(4): 264-275.
[74] NI J F, RAVISHANKAR C V. PA-tree: a parametric indexing scheme for spatio-temporal trajectories[C]//Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer, 2005: 254-272.
[75] CHEN X Y, ZHANG C, GE B, et al. Efficient historical query in HBase for spatio-temporal decision support[J]. International Journal of Computers Communications & Control, 2016, 11(5): 613.
[76] 冯钧, 李顶圣, 陆佳民, 等. 基于HBase的路网移动对象时空索引方法[J]. 计算机应用, 2018, 38(6): 1575-1583.
FENG J, LI D S, LU J M, et al. Spatio-temporal index method for moving objects in road network based on HBase[J]. Journal of Computer Applications, 2018, 38(6): 1575-1583.
[77] SKOVSGAARD A, SIDLAUSKAS D, JENSEN C S. Scalable top-k spatio-temporal term querying[C]//Proceedings of the 2014 IEEE 30th International Conference on Data Engineering. Piscataway: IEEE, 2014: 148-159.
[78] MAGDY A, ALY A M, MOKBEL M F, et al. GeoTrend: spatial trending queries on real-time microblogs[C]//Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2016: 1-10.
[79] AHMED P, HASAN M, KASHYAP A, et al. Efficient computation of top-k frequent terms over spatio-temporal ranges[C]//Proceedings of the 2017 ACM International Conference on Management of Data. New York: ACM, 2017: 1227-1241.
[80] 李晨, 申德荣, 寇月, 等. 多样性感知的时空文本信息的KNN查询处理方法[J]. 模式识别与人工智能, 2017, 30(1): 64-72.
LI C, SHEN D R, KOU Y, et al. Diversity-aware KNN query processing approaches for temporal spatial textual content[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(1): 64-72.
[81] ZHENG K, SHANG S, YUAN N J, et al. Towards efficient search for activity trajectories[C]//Proceedings of the 2013 IEEE 29th International Conference on Data Engineering. Piscataway: IEEE, 2013: 230-241.
[82] CHEN W, ZHAO L, XU J J, et al. Ranking based activity trajectory search[C]//Proceedings of the 15th International Conference on Web Information Systems Engineering. Cham: Springer, 2014: 170-185.
[83] WANG S, BAO Z F, CULPEPPER J S, et al. Answering top-k exemplar trajectory queries[C]//Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering. Piscataway: IEEE, 2017: 597-608.
[84] LIU H W, XU J J, ZHENG K, et al. Semantic-aware query processing for activity trajectories[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. New York: ACM, 2017: 283-292.
[85] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[86] GIONIS A, INDYK P, MOTWANI R. Similarity search in high dimensions via hashing[C]//Proceedings of the 25th International Conference on Very Large Data Bases. San Francisco: Morgan Kaufmann, 1999: 518-529.
[87] WU X, YU J K, ZHAO X M. Spatio-temporal keyword query in semantic trajectories[J]. Frontiers of Computer Science, 2021, 16(2): 162602.
[88] 孟祥福, 王丹丹, 张峰. 空间关键字查询综述[J]. 计算机工程与应用, 2021, 57(20): 13-24.
MENG X F, WANG D D, ZHANG F. Overview of spatial keyword queries[J]. Computer Engineering and Applications, 2021, 57(20): 13-24.
[89] ALMASLUKH A, LIU Y Y, MAGDY A. Scalable spatio-temporal top-k interaction queries on dynamic communities[J]. ACM Transactions on Spatial Algorithms and Systems, 2024, 10(1): 1-25.
[90] SARAVANOS C, DRAKOPOULOS G, KANAVOS A, et al. Discovering influential twitter authors via clustering and ranking on apache storm[C]//Proceedings of the 2021 12th International Conference on Information, Intelligence, Systems & Applications. Piscataway: IEEE, 2021: 1-8.
[91] YU Z, LIU Y, YU X, et al. Scalable distributed processing of k nearest neighbor queries over moving objects[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(5): 1383-1396.
[92] CAI R C, LU Z J, WANG L, et al. DITIR: distributed index for high throughput trajectory insertion and real-time temporal range query[J]. Proceedings of the VLDB Endowment, 2017, 10(12): 1865-1868.
[93] HAN L P, HUANG L, YANG X Y, et al. A novel spatio-temporal data storage and index method for ARM-based hadoop server[C]//Proceedings of the 2nd International Conference on Cloud Computing and Security. Cham: Springer, 2016: 206-216.
[94] JACKINS C L, TANIMOTO S L. Oct-trees and their use in representing three-dimensional objects[J]. Computer Graphics and Image Processing, 1980, 14(3): 249-270.
[95] WANG H Z, BELHASSENA A. Parallel trajectory search based on distributed index[J]. Information Sciences, 2017, 388: 62-83.
[96] BARECHE I, XIA Y. A distributed hybrid indexing for continuous KNN query processing over moving objects[J]. ISPRS International Journal of Geo-Information, 2022, 11(4): 264.
[97] ALI M E, EUSUF S S, ISLAM K A. An efficient index for contact tracing query in a large spatio-temporal database[EB/OL]. [2024-09-14]. https://arxiv.org/abs/2006.12812.
[98] EUSUF S S, ISLAM K A, ALI M E, et al. A web-based system for efficient contact tracing query in a large spatio-temporal database[C]//Proceedings of the 28th International Conference on Advances in Geographic Information Systems. New York: ACM, 2020: 473-476.
[99] LI Z H, ZUO J K, SONG M X, et al. Query and clustering of spatio-temporal trajectory big data under the background of COVID-19[C]//Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics. New York: ACM, 2021: 676-680.
[100] WANG Z, YUAN Y, CHANG L, et al. A graph-based visual query method for massive human trajectory data[J]. IEEE Access, 2019, 7: 160879-160888.
[101] HUANG Z, ZHAO Y, CHEN W, et al. A natural-language-based visual query approach of uncertain human trajectories[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 26(1): 1256-1266.
[102] WU S, PANG Z F, CHEN G, et al. NEIST: a neural-
enhanced index for spatio-temporal queries[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(4): 1659-1673.
[103] LISI C, SHUO S, CHENGCHENG Y, et a1. Spatial keyword search: a survey[J]. Geolnformatica, 2020, 24(1): 85-106.
[104] YANG M M, GUO T L, ZHU T Q, et al. Local differential privacy and its applications: a comprehensive survey[J]. Computer Standards & Interfaces, 2024, 89: 103827.
[105] WU S, WANG X L, WANG S, et al. K-anonymity for crowdsourcing database[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(9): 2207-2221.
[106] MIAO Y B, YANG Y T, LI X H, et al. Comprehensive survey on privacy-preserving spatial data query in transportation systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 13603-13616.
[107] WANG J B, CAI Z P, YU J G. Achieving personalized k-anonymity-based content privacy for autonomous vehicles in CPS[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6): 4242-4251.
[108] RODRIGUES S. The design of interactive spatio-temporal information visualization-a conceptual model[C]//Proceedings of the 2023 27th International Conference on Information Visualisation. Piscataway: IEEE, 2023: 155-160.
[109] KRASKA T, BEUTEL A, CHI E H, et al. The case for learned index structures[C]//Proceedings of the 2018 International Conference on Management of Data. New York: ACM, 2018: 489-504.