空间数据库移动对象轨迹和查询技术研究
|
摘要
空间数据库移动对象的查询技术具有重要的研究意义,为了增强空间数据库对时空对象的处理能力,本论文对空间数据库移动对象的位置和轨迹的索引、查询和智能分析技术进行了系统地研究。主要取得了以下研究成果:
系统研究了关于移动对象的高级索引技术。重点给出了Hibert R树,X树,TR树和LUR树等复杂的空间索引结构。研究了全时态索引结构原理,提出了能支持全时态信息处理的索引结构TB+_TPR*-tree,基于TB+_TPR*-tree,设计了高效率的更新、插入及删除算法。进一步给出了二级索引结构的模型和操作算法。该结构能较好地对移动对象轨迹的过去、现在和将来进行查询。
对移动环境下带有约束条件的移动查询进行了基础性研究,给出了网络和轨迹的映射方法,分析了查询映射思想。介绍了高效的将来轨迹的四叉树和新的插入﹑删除和更新算法,提高了对移动对象进行查询的效率。
移动环境下的时空最近邻查询具有重要的研究意义。介绍了利用启发式规则计算时空道路网络里最近邻的方法,对查询模式及路径查询代价进行了详细分析,分析了选择移动查询点及其最近邻的启发式规则,阐述了p区域和r区域的概念特点,设计了在时空道路网络里进行移动对象的最近邻查询的高效算法;同时又论述了时空道路网络里基于Voronoi图计算移动对象的最近邻的方法。时空道路网络里的研究工作增强了时空移动对象数据库查询和监测路网中移动对象的能力。
进一步对时空对象查询进行了有意义的扩展性研究。给出了典型曲面上的最近邻查询方法和思想。将柱面及锥面上的空间查询问题完好地变通为求解有界平面内数据点集的最近邻问题。给出了反向最远邻的过滤和查询方法,全新构建了适合反向最远邻查询的空间索引结构。该部分的研究成果扩展了时空数据库处理空间对象所发出的较为复杂的查询需求的能力,具有较高的理论和实际意义。
Research on querying technology for moving objects in spatial database is significant. To improve spatial database’s ability of managing spatio-temporal objects, this paper systematically investigates the indexing, querying and intellectual analysis technology for moving objects’location and trajectory in spatial database. The main research is as follows.
The paper systematically investigates advanced indexing technology for moving objects. The paper presents complex spatial indexing structure such as Hiber R tree, X tree, TR tree and LUR tree, etc. It investigates principle of total-temporal indexing structure, and gives the TB+_TPR*-tree indexing structure which can support total-temporal information management. Based on TB+_TPR*-tree, highly efficient updating, insertion and deletion algorithms are designed. It further gives the two-level indexing structure’s model and operation algorithms. The structure can preferably query moving objects’past, current and future trajectory.
The paper investigates moving queries with restrictions in moving environment, presents mapping method for network and trajectory, analyzes query mapping idea. It also introduces efficient quadtree for future trajectory, adopts new insertion, deletion and updating algorithms, greatly improves the query efficiency of moving objects.
It’s important to investigate the spatio-temporal nearest neighbors in moving environment. This paper introduces heuristic-rule to compute the nearest neighbors in spatio-temporal road network, detailedly analyzes the query mode and route query cost. It introduces heuristic-rule to select moving query point and its nearest neighbors, sets forth the concepts of p-region and r-region and develops the efficient arithmetic to deal with the nearest neighbors query for the moving objects in the spatio-temporal road network. It also discusses the method of computing moving objects’nearest neighbors using Voronoi diagram in spatio- temporal road network. These researches improve the abilities to query spatio-temporal moving object database and supervise the moving objects in road network.
This paper futher extendly investigates spatio-temporal object’s query method. It presents method and ideas of the nearest neighbor queries on the typical curved surface. It perfectly turns the spatial queries on cylinder and cone into computing the nearest neighbors of the data point aggregate in limitary plane. The paper gives reverse furthest neighbors’filter and query method, constructs new spatial indexing structure suitable for reverse furthest neighbors’query. This section extends the spatio-temporal database’s ability to manage more complex query requirement. It has higher theoretical and practical significance.
系统研究了关于移动对象的高级索引技术。重点给出了Hibert R树,X树,TR树和LUR树等复杂的空间索引结构。研究了全时态索引结构原理,提出了能支持全时态信息处理的索引结构TB+_TPR*-tree,基于TB+_TPR*-tree,设计了高效率的更新、插入及删除算法。进一步给出了二级索引结构的模型和操作算法。该结构能较好地对移动对象轨迹的过去、现在和将来进行查询。
对移动环境下带有约束条件的移动查询进行了基础性研究,给出了网络和轨迹的映射方法,分析了查询映射思想。介绍了高效的将来轨迹的四叉树和新的插入﹑删除和更新算法,提高了对移动对象进行查询的效率。
移动环境下的时空最近邻查询具有重要的研究意义。介绍了利用启发式规则计算时空道路网络里最近邻的方法,对查询模式及路径查询代价进行了详细分析,分析了选择移动查询点及其最近邻的启发式规则,阐述了p区域和r区域的概念特点,设计了在时空道路网络里进行移动对象的最近邻查询的高效算法;同时又论述了时空道路网络里基于Voronoi图计算移动对象的最近邻的方法。时空道路网络里的研究工作增强了时空移动对象数据库查询和监测路网中移动对象的能力。
进一步对时空对象查询进行了有意义的扩展性研究。给出了典型曲面上的最近邻查询方法和思想。将柱面及锥面上的空间查询问题完好地变通为求解有界平面内数据点集的最近邻问题。给出了反向最远邻的过滤和查询方法,全新构建了适合反向最远邻查询的空间索引结构。该部分的研究成果扩展了时空数据库处理空间对象所发出的较为复杂的查询需求的能力,具有较高的理论和实际意义。
Research on querying technology for moving objects in spatial database is significant. To improve spatial database’s ability of managing spatio-temporal objects, this paper systematically investigates the indexing, querying and intellectual analysis technology for moving objects’location and trajectory in spatial database. The main research is as follows.
The paper systematically investigates advanced indexing technology for moving objects. The paper presents complex spatial indexing structure such as Hiber R tree, X tree, TR tree and LUR tree, etc. It investigates principle of total-temporal indexing structure, and gives the TB+_TPR*-tree indexing structure which can support total-temporal information management. Based on TB+_TPR*-tree, highly efficient updating, insertion and deletion algorithms are designed. It further gives the two-level indexing structure’s model and operation algorithms. The structure can preferably query moving objects’past, current and future trajectory.
The paper investigates moving queries with restrictions in moving environment, presents mapping method for network and trajectory, analyzes query mapping idea. It also introduces efficient quadtree for future trajectory, adopts new insertion, deletion and updating algorithms, greatly improves the query efficiency of moving objects.
It’s important to investigate the spatio-temporal nearest neighbors in moving environment. This paper introduces heuristic-rule to compute the nearest neighbors in spatio-temporal road network, detailedly analyzes the query mode and route query cost. It introduces heuristic-rule to select moving query point and its nearest neighbors, sets forth the concepts of p-region and r-region and develops the efficient arithmetic to deal with the nearest neighbors query for the moving objects in the spatio-temporal road network. It also discusses the method of computing moving objects’nearest neighbors using Voronoi diagram in spatio- temporal road network. These researches improve the abilities to query spatio-temporal moving object database and supervise the moving objects in road network.
This paper futher extendly investigates spatio-temporal object’s query method. It presents method and ideas of the nearest neighbor queries on the typical curved surface. It perfectly turns the spatial queries on cylinder and cone into computing the nearest neighbors of the data point aggregate in limitary plane. The paper gives reverse furthest neighbors’filter and query method, constructs new spatial indexing structure suitable for reverse furthest neighbors’query. This section extends the spatio-temporal database’s ability to manage more complex query requirement. It has higher theoretical and practical significance.
引文
[1]万元珍,冯玉才,李东.移动对象数据库:问题及其解决方案[J].计算机科学,2000,27(8):23-26.
[2] Wolfson O, Xu B, Chamberlain S. Moving Objects Databases: Issues and Solutions[C]. Rafanelli M, Jarke M. Proceedings of the 10th International Conference on Scientific and Statistical Database Management (SSDBM98), Capri, Italy,1998:111-122.
[3] Wolfson O. Moving Objects Information Management: the Database Challenge[C]. Y.Halevy A, Gal A. Proceedings of the 5th Workshop on Next Generation Information Technologies and Systems (NGITS’2002), Caesarea, Israel,2002:75-89.
[4]孟晓峰,周龙骤,王珊.数据库技术发展趋势[J] .软件学报,2004,15(12):1822-1835.
[5] Trajcevski G, Wolfson O, Hinrichs K. Managing Uncertainty in Moving Objects Databases[J]. ACM Transactions on Database Systems, 2004,29(3):463-507.
[6] Wolfson O, Chamberlain S, Dao S. Location Management in Moving Objects Databases[C]. WOSBIS, Budapest, Hungary, 1997:7-14.
[7]吕志平,赵冬青,徐爱民等.位置服务系统(LBS)的构建[J].测绘科学,2005,30(2):92-94.
[8] Ilarri S, Wolfson O, Mena E. Processing of Data Streams with Prediction Functions[C]. HICSS. Washington, DC, USA: IEEE Computer Society, 2006:237a-237a.
[9] Sistla A P, Wolfson O, Chamberlain S. Modeling and Querying Moving Objects[C]. Gray W A, Larson P. Proceedings of the Thirteenth International Conference on Data Engineering, Birmingham, UK,1997:422-432.
[10] Cotelo Lema J A, Forlizzi L, Güting R H. Algorithms for Moving Objects Databases[J]. The Computer Journal, 2003,46(6):680-712.
[11] Forlizzi L, Güting R H, Nardelli E. A Data Model and Data Structures for Moving Objects Databases[C]. Chen W D, F. Naughton J, A.Bernstein P. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA,2000:319-330.
[12] Güting R H, de Almeida V T, Ding Z. Modeling and Querying Moving Objects in Networks[J]. The VLDB Journal, 2006,15(2):165-190.
[13] Guttman A. R-trees: a Dynamic Index Structure for Spatial Searching[J]. The SIGMOD Record, 1984,14(2):47-57.
[14] Tayeb J, Ulusoy ?, Wolfson O. A Quadtree-Based Dynamic Attribute Indexing Method[J]. The Computer Journal,1998,41(3):185-200.
[15] Nievergelt J, Hinterberger H, C.Sevcik K. The Grid File: an Adaptable, Symmetric Multikey File Structure[J]. ACM Transactions on Database Systems, 1984,9(1):38-71.
[16] Ding R, Meng X F, Bai Y. Efficient Index Update for Moving Objects with Future Trajectories[C]. Database Systems for Advanced Applications. Washington, DC, USA: IEEE Computer Society, 2003:183-191.
[17] Meng X F, Chen J D. Indexing Future Trajectories of Moving Objects in a Constrained Network[J]. Journal of Computer Science and Technology, 2007, 22(2):245-251.
[18] Tao Y F, Papadias D. The MV3R-Tree: a Spatio-temporal Access Method for Timestamp and Interval Queries[C]. M.G.Apers P, Atzeni P, Ceri S, Paraboschi S, Ramamohanarao K, T.Snodgrass R. Proceedings of 27th Very Large Data Bases Conference, Roma,Italy, 2001:431-440.
[19] Tao Y F, Papadias D, Sun J M. The TPR*-Tree: an Optimized Spatio-temporal Access Method for Predictive Queries[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G.Selinger P, Heuer A. Proceedings of the 29th International Conference on Very Large Data bases, Berlin, Germany, 2003:790-801.
[20] Pfoser D, S.Jensen C, Theodoridis Y. Novel Approaches in Query Processing for Moving Objects Trajectories[C]. Very Large Data Bases. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc,2000:395-406.
[21] ?altenis S, S. Jensen C, T.Leutenegger S. Indexing the Positions of Continuously Moving Objects[C]. Chen W D, F.Naughton J, A.Bernstein P. Proceedings of the ACM SIGMOD International Conference on Management of Data,Dallas, Texas, United States, 2000:331-342.
[22] ?altenis S, S.Jensen C. Indexing of Moving Objects for Location-based Services[C]. ICDE. Washington, DC, USA: IEEE Computer Society,2002:463-472.
[23] Pelanis M, ?altenis S, S. Jensen C. Indexing the Past, Present, and Anticipated Future Positions of Moving Objects[J]. ACM Transactions on Database Systems, 2006,31(1):255-298.
[24] Theodoridis Y, Vazirgiannis M, Sellis T. Spatio-temporal Indexing for Large Multimedia Applications[J]. Multimedia Systems, 1998,6(4): 284-298.
[25] A.Nascimento M, R.O.Silva J, Theodoridis Y. Evaluation of Access Structures for Discretely Moving Points[C]. H.B?hlen M, S.Jensen C, Scholl M. Proceedings of the International Workshop on Spatio-Temporal Database Management, Edinburgh, Scotland, 1999:171-188.
[26] Güting R H, de Almeida V T. Indexing the Trajectories of Moving Objects in Networks[J]. GeoInformatica, 2005,9(1):33-60.
[27] Lee E J, Ho Ryu K, Woo Nam K. Indexing for Efficient Managing Current and Past Trajectory of Moving Object[C]. Yu J X, Lin X M, Lu H J, Zhang Y C. Advanced Web Technologies and Applications, the 6th Asia–Pacific Web Conference, Hangzhou, China,2004:782-787.
[28] Lee E J, Jung Y J, Ho Ryu K. A Moving Point Indexing Using Projection Operation for Location Based Services[C]. Lee Y J, Li J Z, Whang K Y, Lee D. Proceedings of the 9th International Conference on Database Systems for Advanced Applications, Jeju Island, Korea,2004:775-786.
[29] Min J K, Cho H J, Chung C W. An Adaptive Indexing Technique Using Spatio–temporal Query Workloads[J]. Information and Software Technology, 2004,46(4):229-241.
[30] Kwon D, Lee S J, Choi W. An Adaptive Hashing Technique for Indexing Moving Objects[J]. Data and Knowledge Engineering,2006,56(3): 287-303.
[31] Wu W, Guo W Y, Tan K L. Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects[C]. ICDE, Istanbul, Turkey,2007:1116-1125.
[32] Kollios G, Gunopulos D, J.Tsotras V. Nearest Neighbor Queries in a Mobile Environment[C]. H.B?hlen M, S.Jensen C, Scholl M. Lecture Notes In Computer Science, Proceedings of the International Workshop on Spatio-Temporal Database Management,Edinburgh,Scotland, 1999:119-134.
[33] Song Z X, Roussopoulos N. K-Nearest Neighbor Search for Moving QueryPoint[C]. S.Jensen C, Schneider M, Seeger B, J.Tsotras V. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, Redondo Beach, CA, USA,2001:79-96.
[34] Zheng B H, Lee D L. Semantic Caching in Location-Dependent Query Processing[C]. S.Jensen C, Schneider M, Seeger B, J.Tsotras V. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, Redondo Beach, CA, USA,2001:97-113.
[35] Benetis R, S.Jensen C, Karciauskas G,. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects[C]. A.Nascimento M, ?zsu M T, R.Za?ane O. International Database Engineering and Applications Symposium, Edmonton, Canada, 2002:44-53.
[36] Tao Y F, Papadias D. Time-parameterized Queries in Spatio-temporal Databases[C]. International Conference on Management of Data. New York, NY, USA: ACM, 2002:334-345.
[37] Papadias D, Zhang J, Mamoulis N. Query Processing in Spatial Network Databases[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases, Berlin, Germany, 2003:802-813.
[38] S.Iwerks G, Samet H, Smith K. Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases (VLDB), Berlin, Germany,2003:512-523.
[39] Lee M L, Hsu W, S.Jensen C. Supporting Frequent Updates in R-Trees: a Bottom-Up Approach[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases (VLDB), Berlin, Germany,2003:608-619.
[40] Benetis R, S.Jensen C, Karciauskas G. Nearest and Reverse Nearest Neighbor Queries for Moving Objects[J]. The VLDB Journal, 2006,15(3): 229-249.
[41] Wei Wu, Wenyuan Guo, Kian-Lee Tan. Distributed processing of moving K-nearest neighbor query on moving object[C]. IEEE 23rd InternationalConference on Data Engineering, 2007,(ICDE 2007),2007.1116-1125
[42] Kyriakos Mouratidis, Dimitris Papadias. Continuous Nearest Neighbor Queries over Sliding Windows[C]. IEEE Transactions on Knowledge and Data Engineering, June 2007, doi:10.1109/TKDE.2007, 19(6):789-803
[43] Mouratidis K, Papadias D, Bakiras S. A threshold-based algorithm for continuous monitoring of k nearest neighbors[C]. IEEE Transactions on Knowledge and Data Engineering. 2005,17(11):1451-1464
[44] Xiaohui Yu, Ken Q.Pu, Nick Koudas. Monitoring k-Nearest neighbor queries over moving objects[C]. 21st International Conference on Data Engineering(ICDE’05), 2005.631-642
[45] Xiong X, Mokbel M F, Aref W G. SEA-CNN: Scalable processing of continuous k nearest neighbor queries in spatio-temporal databases[C]. Proceedings 21st International Conference on Data Engineering, 2005.643-654.
[46] Cho H J, Chung C W. An Efficient and Scalable Approach to CNN Queries in a Road Network[C]. B?hm K, S.Jensen C, M.Haas L, L.Kersten M, Larson P, Chin Ooi B. Proceedings of the 31st International Conference on Very Large Data Bases(VLDB), Trondheim, Norway, 2005:865-876.
[47] Huang X G, S.Jensen C, ?altenis S. Multiple k Nearest Neighbor Query Processing in Spatial Network Databases[C]. Manolopoulos Y, Pokorny J, K.Sellis T. Advances in Databases and Information Systems: Proceedings of 10th East European Conference, ADBIS, Thessaloniki, Greece, 2006:266-281.
[48] Yunjun Gao, Chun Li, Gencai Chen. Efficient k-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories[J]. Journal of Computer Science and Technology (JCST), 22(2): 232-244.
[49]高云君,李春,陈根才.一种有效的历史移动对象轨迹的k最近邻居查询算法[J].计算机研究与发展,2006, pp. 490-497.
[50] Yunjun Gao, Chun Li, Gencai Chen. Efficient algorithms for historical continuous kNN query processing over moving object trajectories[C]. ln:Proceedings of the Joint Conference of the 9th Asia-Pacific Web Conference and the 8th International Conference on Web-Age Information Management(APWeb/WAIM 2007), 16-18 June, 2007, Huangshan, China,LNCS 4505, pp.188-199.
[51] Yunjun Gao, Chun Li, Gencai Chen. BFPkNN: An Efficient k-Nearest-Neighbor Search Algorithm for Historical Moving Object Trajectories[C]. In: Proceedings of the 4th Biennial International Conference on Advances in Information Systems (ADVIS 2006), 18-20 October, 2006, Izmir, "Turkey, LNCS 4243, pp. 70-79.
[52] Yunjun Gao, Baihua Zheng, Wang-Chien Lee. Continuous Visible Nearest Neighbor Queries[C]. ACM.EDBT 2009, 2009, Saint Petersburg, Russia: 185-194.
[53] Yunjun Gao, Gencai Chen, Qing Li. Constrained k-Nearest Neighbor Query Processing over Moving Object Trajectories[C]. DASFAA 2008: 635-643.
[54] Yunjun Gao, Jing Zhang, Gencai Chen. Optimal-Nearest-Neighbor Queries[C]. ICDE 2008: 1454-1456.
[55] Yunjun Gao, Gencai Chen, Qing Li. Processing Mutual Nearest Neighbor Queries for Moving Object Trajectories[C]. MDM 2008: 116-123.
[56]郝忠孝,刘永山.空间对象的反最近邻查询[J] .计算机科学,2005,32(11):115-118
[57]徐红波,郝忠孝.基于Hilbert曲线的高维k-最近对查询算法[J].计算机工程,2008,34(2):17-19
[58]徐红波,郝忠孝.基于Hilbert曲线的近似k-最近邻查询算法[J].计算机工程,2008,34(12):47-49
[59]徐红波,郝忠孝.一种基于Z曲线近似k-最近对查询算法[J].计算机研究与发展,2008,45(2):310-317
[60]李松,郝忠孝.基于Voronoi图的反向最近邻查询方法研究[J].哈尔滨工程大学学报,2008,29(3):261-265
[61]李松,郝忠孝.移动对象的动态反向最近邻查询技术[J].计算机工程,2008,34(10):40-42
[62]李博涵,郝忠孝.近似查询中重叠区域的扫描计算[J].计算机工程,2008,34(13):10-12
[63] Yunjun Gao, Baihua Zheng, Gencai Chen. Visible Reverse k-Nearest Neighbor Queries[C]. ICDE 2009: 1203-1206.
[64] JoséR, Moreira A, Rodrigues H. The AROUND Architecture for Dynamic Location-Based Services[J]. Mobile Networks and Applications,2003,8(4):377-387.
[65] Kaasinen E. User Needs for Location-aware Mobile Services[J]. Personal and Ubiquitous Computing, 2003,7(1): 70-79.
[66] Civilis A, S.Jensen C, Nenortaite J, Pakalnis S. Efficient Tracking of Moving Objects with Precision Guarantees[C]. First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04). Washington, DC, USA: IEEE Computer Society, 2004:164-173.
[67] Erwig M, Güting R H, Schneider M. Spatio-temporal Data Types: an Approach to Modeling and Querying Moving Objects in Databases[J]. GeoInformatica, 1999,3(3):269-296.
[68]翁敬农.移动对象及其时空模型的研究[C].中国地理信息系统协会编.中国地理信息系统协会第九届年会论文集.中国北京:中国地理信息系统协会,2005:666-672.
[69] mielinski T, R.Badrinath B. Data Management for Mobile Computing[J]. SIGMOD Record,1993,22(1):34-39.
[70] H.Dunham M, Helal A. Mobile Computing and Databases: Anything New?[J]. SIGMOD Record, 1995,24(4):5-9.
[71] BarbaráD, Imielinski T. Sleepers and Workaholics: Caching Strategies in Mobile Environments[J]. The VLDB Journal, 1995,4(4):567-602.
[72] Wolfson O, Chamberlain S, Dao S. Cost and Imprecision in Modeling the Position of Moving Objects[C]. ICDE. Washington, DC, USA: IEEE Computer Society, 1998:588-596.
[73] Wolfson O, Mena E. Applications of Moving Objects Databases. Chapter in the book "Spatial Databases: Technologies, Techniques and Trends"[M].Hershey,USA: the Idea Group Inc,2005:186-203.
[74] Güting R H, Koubarakis M, Sellis T. Spatio-Temporal Databases: The CHOROCHRONOS Approach[M]. Berlin, Germany: Springer,2003:20-25.
[75] Güting R H, H.B?hlen M, Erwig M. A Foundation for Representing and Querying Moving Objects[J]. ACM Transactions on Database Systems (TODS), 2000,25(1):1-42.
[76] Grumbach S, Rigaux P, Segoufin L. The DEDALE System for Complex Spatial Queries[C]. M.Haas L, Tiwary A. Proceedings of the ACMSIGMOD International Conference on Management of Data, Seattle, Washington, United States, 1998:213-224.
[77] Rigaux P, Scholl M, Segoufin L. Building a Constraint-Based Spatial Database System: Model, Languages, and Implementation[J]. Information Systems, 2003,28(6):563-595.
[78] Grumbach S, Rigaux P, Segoufin L. Spatio-Temporal Data Handling with Constraints[J]. Geoinformatica, 2001,5(1):95-115.
[79] SACK J R, URRUTIA J. Voronoi Diagrams[M]. Handbook on Computational Geometry. Ottawa: Elsevier Science, 2000, 201-290.
[80] Tang Y S. The New Method of Getting the Value of Smoothing Constant (in Chinese)[J]. Statistics and Information, 1997,(3):15-18.
[81] Yu B, Kim S H, Bailey T. Curve-Based Representation of Moving Object Trajectories[C]. IDEAS. Washington, DC, USA: IEEE Computer Society, 2004:419-425.
[82] A.Nascimento M, R.O.Silva J. Towards Historical R-trees[C]. Symposium on Applied Computing. New York, NY, USA: ACM, 1998:235-240.
[83] Tao Y F, Papadias D. Efficient Historical R-trees[C]. SSDBM. Washington, DC, USA: IEEE Computer Society, 2001:223-232.
[84] Xu X M, Han J W, Lu W. RT-Tree: An Improved R-Tree Index Structure for Spatiotemporal Databases[C]. Proceedings of the 4th International Symposium on Spatial Data Handling(SDH),1990:1040-1049.
[85] C.Everspaugh A, Chakka V P, M.Patel J. Indexing Large Trajectory Data Sets With SETI[C]. Harizopoulos S, Ailamaki A. Proceedings of the First Biennial Conference on Innovative Data Systems Research, Asilomar, CA, 2003:164-175.
[86]廖巍,熊伟,景宁等.移动对象索引技术研究进展[J].计算机科学,2006,33(8):166-169.
[87] Song Z X, Roussopoulos N. SEB-tree: An Approach to Index Continuously Moving Objects[C]. Chen M S, K.Chrysanthis P, Sloman M, B.Zaslavsky A. Proceedings of the 4th International Conference on Mobile Data Management, Melbourne, Australia,2003:340-344.
[88] Abdelguerfi M, Givaudan J, Shaw K. The 2-3 TR-tree, a Trajectory-oriented Index Structure for Fully Evolving Valid-time Spatio-temporal Datasets[C].Voisard A, Chen S C. Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, McLean, Virginia, USA,2002:29-34.
[89] Song Z X, Roussopoulos N. Hashing Moving Objects[C]. Tan K L, Franklin M J, C. S. Lui J. Proceedings of the Second International Conference on Mobile Data Management, Hong Kong, China,2001:161-172.
[90] Xia Y N, Prabhakar S. Q+R-tree: Efficient Indexing for Moving Object Databases[C]. DASFAA. Washington, DC, USA: IEEE Computer Society, 2003:175-182.
[91] Patel J M, Chen Y, Chakka V P. STRIPES: An Efficient Index for Predicted Trajectories[C]. Weikum G, Christian K?nig A, De?loch S. Proceedings of the ACM SIGMOD International Conference on Management of Data, Paris, France, 2004:635-646.
[92] Prabhakar S, Xia Y N, Kalashnikov D V. Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects[J]. IEEE Transactions on Computers,2002,51(10):1124-1140.
[93] Procopiuc C M, Agarwal P K, Sariel H P. STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects[C]. Mount D M, Stein C. Proceedings of 4th International Workshop on Algorithm Engineering and Experimentation, San Francicsco, CA, USA, 2002:178-193.
[94] BERCHTOLD S, KEIM D A, KRIEGEL H P. The X-Tree: An Index Structure for High-Dimensional Data[C]. In VLDB Conference, 1996, 28-39.
[95] Jagadish H V. A Retrieval Technique for Similar Shapes[J]. ACM SIGMOD Record,1991,20(2):208-217.
[96]李松,郝忠孝.移动查询点的最近邻查询方法研究[J].齐齐哈尔大学学报,2005,21(2):57-59.
[97] Kolahdouzan M, Shahabi C. Voronoi-based K-Nearest Neighbor Search for Spatial Network Databases[C]. A.Nascimento M, ?zsu M T, Kossmann D, J.Miller R, A.Blakeley J, Schiefer K B. Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, Canada, 2004:840-851.
[98] X. Hao, X. Meng and J. Xu: Continuous Density Queries for MovingObjects[C]. Proceedings of SIGMOD MobiDE2008, 1-8, Vancouver, BC, Canada,2008
[99] Chen and X. Meng: Indexing Future Trajectories of Moving Objects in a Constrained Network. Journal of Computer Science and Technology, Vol.22(2): 245--251, March, 2007.
[100] C. Lai, L. Wang, J. Chen, X. Meng, K. Zeitouni: Effective Density Queries for Moving Objects in Road Networks[C]. In Proceedings of APWeb/WAIM'07, Huangshan, China, June 17-19, 2007.
[101] R. Ding, X. Meng,Y. Bai: Efficient Index Update for Moving Objects with Future Trajectories[C]. In Proceedings of 8th International Conference on Database Systems for Advanced Applications (DASFAA2003), pages 183-194, March 26-28, 2003, Kyoto, Japan.
[102] KOLBE D, ZHU QIANG. On K-Nearest Neighbor Searching in Non-Ordered Discrete Data Spaces[C]. Istanbul, Egypt: In: Proc. the 23rd ICDE, 2007, 426-435.
[103] Kang J M, Mokbel M F, Shekhar S et al. Continuous Evaluation of Monoehromatic and Bichromatic Reverse Nearest Neighbors[C]. In: Proc. of the 23rd IEEE ICDE, 2007, 806-815.
[2] Wolfson O, Xu B, Chamberlain S. Moving Objects Databases: Issues and Solutions[C]. Rafanelli M, Jarke M. Proceedings of the 10th International Conference on Scientific and Statistical Database Management (SSDBM98), Capri, Italy,1998:111-122.
[3] Wolfson O. Moving Objects Information Management: the Database Challenge[C]. Y.Halevy A, Gal A. Proceedings of the 5th Workshop on Next Generation Information Technologies and Systems (NGITS’2002), Caesarea, Israel,2002:75-89.
[4]孟晓峰,周龙骤,王珊.数据库技术发展趋势[J] .软件学报,2004,15(12):1822-1835.
[5] Trajcevski G, Wolfson O, Hinrichs K. Managing Uncertainty in Moving Objects Databases[J]. ACM Transactions on Database Systems, 2004,29(3):463-507.
[6] Wolfson O, Chamberlain S, Dao S. Location Management in Moving Objects Databases[C]. WOSBIS, Budapest, Hungary, 1997:7-14.
[7]吕志平,赵冬青,徐爱民等.位置服务系统(LBS)的构建[J].测绘科学,2005,30(2):92-94.
[8] Ilarri S, Wolfson O, Mena E. Processing of Data Streams with Prediction Functions[C]. HICSS. Washington, DC, USA: IEEE Computer Society, 2006:237a-237a.
[9] Sistla A P, Wolfson O, Chamberlain S. Modeling and Querying Moving Objects[C]. Gray W A, Larson P. Proceedings of the Thirteenth International Conference on Data Engineering, Birmingham, UK,1997:422-432.
[10] Cotelo Lema J A, Forlizzi L, Güting R H. Algorithms for Moving Objects Databases[J]. The Computer Journal, 2003,46(6):680-712.
[11] Forlizzi L, Güting R H, Nardelli E. A Data Model and Data Structures for Moving Objects Databases[C]. Chen W D, F. Naughton J, A.Bernstein P. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA,2000:319-330.
[12] Güting R H, de Almeida V T, Ding Z. Modeling and Querying Moving Objects in Networks[J]. The VLDB Journal, 2006,15(2):165-190.
[13] Guttman A. R-trees: a Dynamic Index Structure for Spatial Searching[J]. The SIGMOD Record, 1984,14(2):47-57.
[14] Tayeb J, Ulusoy ?, Wolfson O. A Quadtree-Based Dynamic Attribute Indexing Method[J]. The Computer Journal,1998,41(3):185-200.
[15] Nievergelt J, Hinterberger H, C.Sevcik K. The Grid File: an Adaptable, Symmetric Multikey File Structure[J]. ACM Transactions on Database Systems, 1984,9(1):38-71.
[16] Ding R, Meng X F, Bai Y. Efficient Index Update for Moving Objects with Future Trajectories[C]. Database Systems for Advanced Applications. Washington, DC, USA: IEEE Computer Society, 2003:183-191.
[17] Meng X F, Chen J D. Indexing Future Trajectories of Moving Objects in a Constrained Network[J]. Journal of Computer Science and Technology, 2007, 22(2):245-251.
[18] Tao Y F, Papadias D. The MV3R-Tree: a Spatio-temporal Access Method for Timestamp and Interval Queries[C]. M.G.Apers P, Atzeni P, Ceri S, Paraboschi S, Ramamohanarao K, T.Snodgrass R. Proceedings of 27th Very Large Data Bases Conference, Roma,Italy, 2001:431-440.
[19] Tao Y F, Papadias D, Sun J M. The TPR*-Tree: an Optimized Spatio-temporal Access Method for Predictive Queries[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G.Selinger P, Heuer A. Proceedings of the 29th International Conference on Very Large Data bases, Berlin, Germany, 2003:790-801.
[20] Pfoser D, S.Jensen C, Theodoridis Y. Novel Approaches in Query Processing for Moving Objects Trajectories[C]. Very Large Data Bases. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc,2000:395-406.
[21] ?altenis S, S. Jensen C, T.Leutenegger S. Indexing the Positions of Continuously Moving Objects[C]. Chen W D, F.Naughton J, A.Bernstein P. Proceedings of the ACM SIGMOD International Conference on Management of Data,Dallas, Texas, United States, 2000:331-342.
[22] ?altenis S, S.Jensen C. Indexing of Moving Objects for Location-based Services[C]. ICDE. Washington, DC, USA: IEEE Computer Society,2002:463-472.
[23] Pelanis M, ?altenis S, S. Jensen C. Indexing the Past, Present, and Anticipated Future Positions of Moving Objects[J]. ACM Transactions on Database Systems, 2006,31(1):255-298.
[24] Theodoridis Y, Vazirgiannis M, Sellis T. Spatio-temporal Indexing for Large Multimedia Applications[J]. Multimedia Systems, 1998,6(4): 284-298.
[25] A.Nascimento M, R.O.Silva J, Theodoridis Y. Evaluation of Access Structures for Discretely Moving Points[C]. H.B?hlen M, S.Jensen C, Scholl M. Proceedings of the International Workshop on Spatio-Temporal Database Management, Edinburgh, Scotland, 1999:171-188.
[26] Güting R H, de Almeida V T. Indexing the Trajectories of Moving Objects in Networks[J]. GeoInformatica, 2005,9(1):33-60.
[27] Lee E J, Ho Ryu K, Woo Nam K. Indexing for Efficient Managing Current and Past Trajectory of Moving Object[C]. Yu J X, Lin X M, Lu H J, Zhang Y C. Advanced Web Technologies and Applications, the 6th Asia–Pacific Web Conference, Hangzhou, China,2004:782-787.
[28] Lee E J, Jung Y J, Ho Ryu K. A Moving Point Indexing Using Projection Operation for Location Based Services[C]. Lee Y J, Li J Z, Whang K Y, Lee D. Proceedings of the 9th International Conference on Database Systems for Advanced Applications, Jeju Island, Korea,2004:775-786.
[29] Min J K, Cho H J, Chung C W. An Adaptive Indexing Technique Using Spatio–temporal Query Workloads[J]. Information and Software Technology, 2004,46(4):229-241.
[30] Kwon D, Lee S J, Choi W. An Adaptive Hashing Technique for Indexing Moving Objects[J]. Data and Knowledge Engineering,2006,56(3): 287-303.
[31] Wu W, Guo W Y, Tan K L. Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects[C]. ICDE, Istanbul, Turkey,2007:1116-1125.
[32] Kollios G, Gunopulos D, J.Tsotras V. Nearest Neighbor Queries in a Mobile Environment[C]. H.B?hlen M, S.Jensen C, Scholl M. Lecture Notes In Computer Science, Proceedings of the International Workshop on Spatio-Temporal Database Management,Edinburgh,Scotland, 1999:119-134.
[33] Song Z X, Roussopoulos N. K-Nearest Neighbor Search for Moving QueryPoint[C]. S.Jensen C, Schneider M, Seeger B, J.Tsotras V. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, Redondo Beach, CA, USA,2001:79-96.
[34] Zheng B H, Lee D L. Semantic Caching in Location-Dependent Query Processing[C]. S.Jensen C, Schneider M, Seeger B, J.Tsotras V. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, Redondo Beach, CA, USA,2001:97-113.
[35] Benetis R, S.Jensen C, Karciauskas G,. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects[C]. A.Nascimento M, ?zsu M T, R.Za?ane O. International Database Engineering and Applications Symposium, Edmonton, Canada, 2002:44-53.
[36] Tao Y F, Papadias D. Time-parameterized Queries in Spatio-temporal Databases[C]. International Conference on Management of Data. New York, NY, USA: ACM, 2002:334-345.
[37] Papadias D, Zhang J, Mamoulis N. Query Processing in Spatial Network Databases[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases, Berlin, Germany, 2003:802-813.
[38] S.Iwerks G, Samet H, Smith K. Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases (VLDB), Berlin, Germany,2003:512-523.
[39] Lee M L, Hsu W, S.Jensen C. Supporting Frequent Updates in R-Trees: a Bottom-Up Approach[C]. Christoph Freytag J, C.Lockemann P, Abiteboul S, J.Carey M, G. Selinger P, Heuer A. Proceedings of 29th International Conference on Very Large Data Bases (VLDB), Berlin, Germany,2003:608-619.
[40] Benetis R, S.Jensen C, Karciauskas G. Nearest and Reverse Nearest Neighbor Queries for Moving Objects[J]. The VLDB Journal, 2006,15(3): 229-249.
[41] Wei Wu, Wenyuan Guo, Kian-Lee Tan. Distributed processing of moving K-nearest neighbor query on moving object[C]. IEEE 23rd InternationalConference on Data Engineering, 2007,(ICDE 2007),2007.1116-1125
[42] Kyriakos Mouratidis, Dimitris Papadias. Continuous Nearest Neighbor Queries over Sliding Windows[C]. IEEE Transactions on Knowledge and Data Engineering, June 2007, doi:10.1109/TKDE.2007, 19(6):789-803
[43] Mouratidis K, Papadias D, Bakiras S. A threshold-based algorithm for continuous monitoring of k nearest neighbors[C]. IEEE Transactions on Knowledge and Data Engineering. 2005,17(11):1451-1464
[44] Xiaohui Yu, Ken Q.Pu, Nick Koudas. Monitoring k-Nearest neighbor queries over moving objects[C]. 21st International Conference on Data Engineering(ICDE’05), 2005.631-642
[45] Xiong X, Mokbel M F, Aref W G. SEA-CNN: Scalable processing of continuous k nearest neighbor queries in spatio-temporal databases[C]. Proceedings 21st International Conference on Data Engineering, 2005.643-654.
[46] Cho H J, Chung C W. An Efficient and Scalable Approach to CNN Queries in a Road Network[C]. B?hm K, S.Jensen C, M.Haas L, L.Kersten M, Larson P, Chin Ooi B. Proceedings of the 31st International Conference on Very Large Data Bases(VLDB), Trondheim, Norway, 2005:865-876.
[47] Huang X G, S.Jensen C, ?altenis S. Multiple k Nearest Neighbor Query Processing in Spatial Network Databases[C]. Manolopoulos Y, Pokorny J, K.Sellis T. Advances in Databases and Information Systems: Proceedings of 10th East European Conference, ADBIS, Thessaloniki, Greece, 2006:266-281.
[48] Yunjun Gao, Chun Li, Gencai Chen. Efficient k-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories[J]. Journal of Computer Science and Technology (JCST), 22(2): 232-244.
[49]高云君,李春,陈根才.一种有效的历史移动对象轨迹的k最近邻居查询算法[J].计算机研究与发展,2006, pp. 490-497.
[50] Yunjun Gao, Chun Li, Gencai Chen. Efficient algorithms for historical continuous kNN query processing over moving object trajectories[C]. ln:Proceedings of the Joint Conference of the 9th Asia-Pacific Web Conference and the 8th International Conference on Web-Age Information Management(APWeb/WAIM 2007), 16-18 June, 2007, Huangshan, China,LNCS 4505, pp.188-199.
[51] Yunjun Gao, Chun Li, Gencai Chen. BFPkNN: An Efficient k-Nearest-Neighbor Search Algorithm for Historical Moving Object Trajectories[C]. In: Proceedings of the 4th Biennial International Conference on Advances in Information Systems (ADVIS 2006), 18-20 October, 2006, Izmir, "Turkey, LNCS 4243, pp. 70-79.
[52] Yunjun Gao, Baihua Zheng, Wang-Chien Lee. Continuous Visible Nearest Neighbor Queries[C]. ACM.EDBT 2009, 2009, Saint Petersburg, Russia: 185-194.
[53] Yunjun Gao, Gencai Chen, Qing Li. Constrained k-Nearest Neighbor Query Processing over Moving Object Trajectories[C]. DASFAA 2008: 635-643.
[54] Yunjun Gao, Jing Zhang, Gencai Chen. Optimal-Nearest-Neighbor Queries[C]. ICDE 2008: 1454-1456.
[55] Yunjun Gao, Gencai Chen, Qing Li. Processing Mutual Nearest Neighbor Queries for Moving Object Trajectories[C]. MDM 2008: 116-123.
[56]郝忠孝,刘永山.空间对象的反最近邻查询[J] .计算机科学,2005,32(11):115-118
[57]徐红波,郝忠孝.基于Hilbert曲线的高维k-最近对查询算法[J].计算机工程,2008,34(2):17-19
[58]徐红波,郝忠孝.基于Hilbert曲线的近似k-最近邻查询算法[J].计算机工程,2008,34(12):47-49
[59]徐红波,郝忠孝.一种基于Z曲线近似k-最近对查询算法[J].计算机研究与发展,2008,45(2):310-317
[60]李松,郝忠孝.基于Voronoi图的反向最近邻查询方法研究[J].哈尔滨工程大学学报,2008,29(3):261-265
[61]李松,郝忠孝.移动对象的动态反向最近邻查询技术[J].计算机工程,2008,34(10):40-42
[62]李博涵,郝忠孝.近似查询中重叠区域的扫描计算[J].计算机工程,2008,34(13):10-12
[63] Yunjun Gao, Baihua Zheng, Gencai Chen. Visible Reverse k-Nearest Neighbor Queries[C]. ICDE 2009: 1203-1206.
[64] JoséR, Moreira A, Rodrigues H. The AROUND Architecture for Dynamic Location-Based Services[J]. Mobile Networks and Applications,2003,8(4):377-387.
[65] Kaasinen E. User Needs for Location-aware Mobile Services[J]. Personal and Ubiquitous Computing, 2003,7(1): 70-79.
[66] Civilis A, S.Jensen C, Nenortaite J, Pakalnis S. Efficient Tracking of Moving Objects with Precision Guarantees[C]. First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04). Washington, DC, USA: IEEE Computer Society, 2004:164-173.
[67] Erwig M, Güting R H, Schneider M. Spatio-temporal Data Types: an Approach to Modeling and Querying Moving Objects in Databases[J]. GeoInformatica, 1999,3(3):269-296.
[68]翁敬农.移动对象及其时空模型的研究[C].中国地理信息系统协会编.中国地理信息系统协会第九届年会论文集.中国北京:中国地理信息系统协会,2005:666-672.
[69] mielinski T, R.Badrinath B. Data Management for Mobile Computing[J]. SIGMOD Record,1993,22(1):34-39.
[70] H.Dunham M, Helal A. Mobile Computing and Databases: Anything New?[J]. SIGMOD Record, 1995,24(4):5-9.
[71] BarbaráD, Imielinski T. Sleepers and Workaholics: Caching Strategies in Mobile Environments[J]. The VLDB Journal, 1995,4(4):567-602.
[72] Wolfson O, Chamberlain S, Dao S. Cost and Imprecision in Modeling the Position of Moving Objects[C]. ICDE. Washington, DC, USA: IEEE Computer Society, 1998:588-596.
[73] Wolfson O, Mena E. Applications of Moving Objects Databases. Chapter in the book "Spatial Databases: Technologies, Techniques and Trends"[M].Hershey,USA: the Idea Group Inc,2005:186-203.
[74] Güting R H, Koubarakis M, Sellis T. Spatio-Temporal Databases: The CHOROCHRONOS Approach[M]. Berlin, Germany: Springer,2003:20-25.
[75] Güting R H, H.B?hlen M, Erwig M. A Foundation for Representing and Querying Moving Objects[J]. ACM Transactions on Database Systems (TODS), 2000,25(1):1-42.
[76] Grumbach S, Rigaux P, Segoufin L. The DEDALE System for Complex Spatial Queries[C]. M.Haas L, Tiwary A. Proceedings of the ACMSIGMOD International Conference on Management of Data, Seattle, Washington, United States, 1998:213-224.
[77] Rigaux P, Scholl M, Segoufin L. Building a Constraint-Based Spatial Database System: Model, Languages, and Implementation[J]. Information Systems, 2003,28(6):563-595.
[78] Grumbach S, Rigaux P, Segoufin L. Spatio-Temporal Data Handling with Constraints[J]. Geoinformatica, 2001,5(1):95-115.
[79] SACK J R, URRUTIA J. Voronoi Diagrams[M]. Handbook on Computational Geometry. Ottawa: Elsevier Science, 2000, 201-290.
[80] Tang Y S. The New Method of Getting the Value of Smoothing Constant (in Chinese)[J]. Statistics and Information, 1997,(3):15-18.
[81] Yu B, Kim S H, Bailey T. Curve-Based Representation of Moving Object Trajectories[C]. IDEAS. Washington, DC, USA: IEEE Computer Society, 2004:419-425.
[82] A.Nascimento M, R.O.Silva J. Towards Historical R-trees[C]. Symposium on Applied Computing. New York, NY, USA: ACM, 1998:235-240.
[83] Tao Y F, Papadias D. Efficient Historical R-trees[C]. SSDBM. Washington, DC, USA: IEEE Computer Society, 2001:223-232.
[84] Xu X M, Han J W, Lu W. RT-Tree: An Improved R-Tree Index Structure for Spatiotemporal Databases[C]. Proceedings of the 4th International Symposium on Spatial Data Handling(SDH),1990:1040-1049.
[85] C.Everspaugh A, Chakka V P, M.Patel J. Indexing Large Trajectory Data Sets With SETI[C]. Harizopoulos S, Ailamaki A. Proceedings of the First Biennial Conference on Innovative Data Systems Research, Asilomar, CA, 2003:164-175.
[86]廖巍,熊伟,景宁等.移动对象索引技术研究进展[J].计算机科学,2006,33(8):166-169.
[87] Song Z X, Roussopoulos N. SEB-tree: An Approach to Index Continuously Moving Objects[C]. Chen M S, K.Chrysanthis P, Sloman M, B.Zaslavsky A. Proceedings of the 4th International Conference on Mobile Data Management, Melbourne, Australia,2003:340-344.
[88] Abdelguerfi M, Givaudan J, Shaw K. The 2-3 TR-tree, a Trajectory-oriented Index Structure for Fully Evolving Valid-time Spatio-temporal Datasets[C].Voisard A, Chen S C. Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, McLean, Virginia, USA,2002:29-34.
[89] Song Z X, Roussopoulos N. Hashing Moving Objects[C]. Tan K L, Franklin M J, C. S. Lui J. Proceedings of the Second International Conference on Mobile Data Management, Hong Kong, China,2001:161-172.
[90] Xia Y N, Prabhakar S. Q+R-tree: Efficient Indexing for Moving Object Databases[C]. DASFAA. Washington, DC, USA: IEEE Computer Society, 2003:175-182.
[91] Patel J M, Chen Y, Chakka V P. STRIPES: An Efficient Index for Predicted Trajectories[C]. Weikum G, Christian K?nig A, De?loch S. Proceedings of the ACM SIGMOD International Conference on Management of Data, Paris, France, 2004:635-646.
[92] Prabhakar S, Xia Y N, Kalashnikov D V. Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects[J]. IEEE Transactions on Computers,2002,51(10):1124-1140.
[93] Procopiuc C M, Agarwal P K, Sariel H P. STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects[C]. Mount D M, Stein C. Proceedings of 4th International Workshop on Algorithm Engineering and Experimentation, San Francicsco, CA, USA, 2002:178-193.
[94] BERCHTOLD S, KEIM D A, KRIEGEL H P. The X-Tree: An Index Structure for High-Dimensional Data[C]. In VLDB Conference, 1996, 28-39.
[95] Jagadish H V. A Retrieval Technique for Similar Shapes[J]. ACM SIGMOD Record,1991,20(2):208-217.
[96]李松,郝忠孝.移动查询点的最近邻查询方法研究[J].齐齐哈尔大学学报,2005,21(2):57-59.
[97] Kolahdouzan M, Shahabi C. Voronoi-based K-Nearest Neighbor Search for Spatial Network Databases[C]. A.Nascimento M, ?zsu M T, Kossmann D, J.Miller R, A.Blakeley J, Schiefer K B. Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, Canada, 2004:840-851.
[98] X. Hao, X. Meng and J. Xu: Continuous Density Queries for MovingObjects[C]. Proceedings of SIGMOD MobiDE2008, 1-8, Vancouver, BC, Canada,2008
[99] Chen and X. Meng: Indexing Future Trajectories of Moving Objects in a Constrained Network. Journal of Computer Science and Technology, Vol.22(2): 245--251, March, 2007.
[100] C. Lai, L. Wang, J. Chen, X. Meng, K. Zeitouni: Effective Density Queries for Moving Objects in Road Networks[C]. In Proceedings of APWeb/WAIM'07, Huangshan, China, June 17-19, 2007.
[101] R. Ding, X. Meng,Y. Bai: Efficient Index Update for Moving Objects with Future Trajectories[C]. In Proceedings of 8th International Conference on Database Systems for Advanced Applications (DASFAA2003), pages 183-194, March 26-28, 2003, Kyoto, Japan.
[102] KOLBE D, ZHU QIANG. On K-Nearest Neighbor Searching in Non-Ordered Discrete Data Spaces[C]. Istanbul, Egypt: In: Proc. the 23rd ICDE, 2007, 426-435.
[103] Kang J M, Mokbel M F, Shekhar S et al. Continuous Evaluation of Monoehromatic and Bichromatic Reverse Nearest Neighbors[C]. In: Proc. of the 23rd IEEE ICDE, 2007, 806-815.