空间数据仓库中查询优化技术研究
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摘要
空间数据仓库的查询性能严重限制了空间数据仓库的使用。本文以商业银行空间数据仓库系统为实际应用背景,根据空间数据仓库中空间数据和SOLAP查询的特点,针对空间数据仓库中查询性能的问题,展开了基于物化视图技术的查询优化方法的研究。
本文对空间区域聚集查询进行了深入的研究,提出了两级物化的方法,该方法能有效地在空间维和非空间维上进行区域聚集查询。其基本思想是先不考虑空间维上的查询区域,把用户常用的非空间维上的区域聚集查询组成一个候选视图集,对候选视图集进行预处理后,利用遗传算法,从中选择出满足存储空间限制的总查询代价最小的视图进行物化,这个过程称为一级物化。针对每一个物化的视图,计算空间维索引R-tree中的每一个中间结点的聚集值,保存到预定义的表中,这个过程称为二级物化。由于二级物化视图中存储了R-tree中间结点的聚集结果,因此查询过程中减少了R-tree中结点的访问次数,以及查询一级视图的时间,从而提高了聚集查询效率。
本文在深入研究聚类技术的基础上,给出了一种适用于高维、稀疏、二值型数据的相似性度量函数,用于对可合并的空间对象组进行聚类。然后针对空间贪心算法中间接收益计算量大的问题,提出了基于聚类的空间贪心算法。该算法在每个聚类中计算合并组的收益,而不是在整个合并组集合中计算,同时,保存每个聚类中收益最大的合并组及收益值,每次选择收益最大的合并组后,只需要重新计算该合并组所在类中的其他合并组的收益,其他聚类中的合并组不需要再重新计算收益,因此大幅度减少了合并组的收益计算量。通过仿真实验说明了该算法的有效性和优越性。
本文给出了空间数据仓库的代价模型,并在此基础上提出了一种视图的动态选择算法。该算法采用实时调整与定期调整共用的策略,首先预留一部分存储空间,用于存储新的视图,当这部分预留的空间用完后,如果这时还需要存储视图时,就逐个淘汰收益小的视图,直至满足空闲空间要求为止。通过实验说明了算法的有效性,并比较了预留不同空闲空间时的算法性能。
The performance of the query restrained the using of spatial data warehouse severely. Based on the background of the real application in the spatial data warehouse of Xuzhou branch of China Construction Bank, this paper analyses the features of spatial data and SOLAP query in spatial data warehouse, focuses on the problems of its query function, and studies the optimization of the Materialized View Query.
This paper analyzes the spatial range aggregate queries, and presents the two-stage materialized method. It processes efficiently range aggregate queries on spatial and non-spatial dimension. The method main idea is as follows, to form the non-spatial Range Aggregate Queries which are frequently used into a candidate view collection without considering the spatial queries, use genetic algorithms to select the view which costs minimal, and materialize the view. This process is called Primary Materialize. After then, calculating the value of each intermediate node in the R-tree in all the materialized view, and saving all the values in the correspondent table. This process is called two-stage Materialize. The Secondary Materialized View contains the results of the intermediate node in the R-tree, so the access times of spatial queries diminished, the query time of primary view shortened, and the efficiency of the aggregate query improved accordingly.
This paper raises a Similarity Measure Function used for multi-dimension, sparse, and binary data based on the intensive analysis for the aggregate techniques; bring forwards spatial Aggregate Greedy Algorithm aimed at the maximal indirect profit in spatial greedy algorithms. The algorithm finds out the spatial object group first in adjacent spaces, that is combination group. The spatial objects in the group can be combined to one. The Algorithm calculates the profit of the combination group in every clustering, rather than calculate it in the whole group. Besides, find the combination group with the maximal profit in the clustering, save the group and its profit. After select the combination group, recalculation for the profit of the combination group in other clustering is not needed, so the calculating work is simplified largely. The effectiveness and advantages of this algorithm is proved by simulations.
This paper presents the Dynamic View Selection Algorithm based on Cost Model of spatial data warehouse. The algorithm preserves some storage space to store new views. If more space is need, delete the less profitable views one by one when the preserved space is used out. Repeat the above step till the need for the idle space is fulfilled. Experiments proved the validity of the algorithm and compared the performance of the algorithms in different preserved time.
本文对空间区域聚集查询进行了深入的研究,提出了两级物化的方法,该方法能有效地在空间维和非空间维上进行区域聚集查询。其基本思想是先不考虑空间维上的查询区域,把用户常用的非空间维上的区域聚集查询组成一个候选视图集,对候选视图集进行预处理后,利用遗传算法,从中选择出满足存储空间限制的总查询代价最小的视图进行物化,这个过程称为一级物化。针对每一个物化的视图,计算空间维索引R-tree中的每一个中间结点的聚集值,保存到预定义的表中,这个过程称为二级物化。由于二级物化视图中存储了R-tree中间结点的聚集结果,因此查询过程中减少了R-tree中结点的访问次数,以及查询一级视图的时间,从而提高了聚集查询效率。
本文在深入研究聚类技术的基础上,给出了一种适用于高维、稀疏、二值型数据的相似性度量函数,用于对可合并的空间对象组进行聚类。然后针对空间贪心算法中间接收益计算量大的问题,提出了基于聚类的空间贪心算法。该算法在每个聚类中计算合并组的收益,而不是在整个合并组集合中计算,同时,保存每个聚类中收益最大的合并组及收益值,每次选择收益最大的合并组后,只需要重新计算该合并组所在类中的其他合并组的收益,其他聚类中的合并组不需要再重新计算收益,因此大幅度减少了合并组的收益计算量。通过仿真实验说明了该算法的有效性和优越性。
本文给出了空间数据仓库的代价模型,并在此基础上提出了一种视图的动态选择算法。该算法采用实时调整与定期调整共用的策略,首先预留一部分存储空间,用于存储新的视图,当这部分预留的空间用完后,如果这时还需要存储视图时,就逐个淘汰收益小的视图,直至满足空闲空间要求为止。通过实验说明了算法的有效性,并比较了预留不同空闲空间时的算法性能。
The performance of the query restrained the using of spatial data warehouse severely. Based on the background of the real application in the spatial data warehouse of Xuzhou branch of China Construction Bank, this paper analyses the features of spatial data and SOLAP query in spatial data warehouse, focuses on the problems of its query function, and studies the optimization of the Materialized View Query.
This paper analyzes the spatial range aggregate queries, and presents the two-stage materialized method. It processes efficiently range aggregate queries on spatial and non-spatial dimension. The method main idea is as follows, to form the non-spatial Range Aggregate Queries which are frequently used into a candidate view collection without considering the spatial queries, use genetic algorithms to select the view which costs minimal, and materialize the view. This process is called Primary Materialize. After then, calculating the value of each intermediate node in the R-tree in all the materialized view, and saving all the values in the correspondent table. This process is called two-stage Materialize. The Secondary Materialized View contains the results of the intermediate node in the R-tree, so the access times of spatial queries diminished, the query time of primary view shortened, and the efficiency of the aggregate query improved accordingly.
This paper raises a Similarity Measure Function used for multi-dimension, sparse, and binary data based on the intensive analysis for the aggregate techniques; bring forwards spatial Aggregate Greedy Algorithm aimed at the maximal indirect profit in spatial greedy algorithms. The algorithm finds out the spatial object group first in adjacent spaces, that is combination group. The spatial objects in the group can be combined to one. The Algorithm calculates the profit of the combination group in every clustering, rather than calculate it in the whole group. Besides, find the combination group with the maximal profit in the clustering, save the group and its profit. After select the combination group, recalculation for the profit of the combination group in other clustering is not needed, so the calculating work is simplified largely. The effectiveness and advantages of this algorithm is proved by simulations.
This paper presents the Dynamic View Selection Algorithm based on Cost Model of spatial data warehouse. The algorithm preserves some storage space to store new views. If more space is need, delete the less profitable views one by one when the preserved space is used out. Repeat the above step till the need for the idle space is fulfilled. Experiments proved the validity of the algorithm and compared the performance of the algorithms in different preserved time.
引文
[1] Gonzales M L. Seeking Spatial Intelligence[J]. Intelligent Enterprise, 2000, 3(2): 1-8.
[2]陈细谦.空间数据仓库关键技术的研究与实现[D].大连:大连理工大学, 2005.
[3]陈细谦,迟忠先,昃宗亮,苏立强.地理编码在空间数据仓库ETL中的应用[J].小型微型计算机系统, 2005, 26(4): 628-630.
[4]陈细谦,迟忠先,金妮.城市地理编码系统应用与研究[J].计算机工程, 2004, 30(23): 50-52.
[5]张宁,贾自艳,史忠植.数据仓库中ETL技术的研究[J].计算机工程与应用, 2002, 38(24): 213-216.
[6]田扬戈,边馥苓.空间数据仓库的ETL研究[J].武汉大学学报:信息科学版, 2007, 31(4): 362-365.
[7] Han J W, Stefanovic N, Kopersky K. Selective materialization: an Efficient Method for Spatial Data Cube Construction[C]. Proceedings of Research and Development in Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 1998: 144-158.
[8] Rivest S, Bedard Y, Marchand P. Towards Better Support for Spatial Decision Making: Defining the Characteristics of Spatial on-line Analytical Processing (SOLAP)[J]. Geomatica, 2001, 55(4): 539-555.
[9] Fidalgo R N, Times V C, Silva J, Souza F. GeoDWFrame: A framework for guiding the design of geographical dimensional schemas[C]. Proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery. Berlin: Springer-Verlag, 2004: 26-37.
[10] Pedersen T, Jensen C, Dyreson C. A foundation for capturing and querying complex multidimensional data[J]. Information Systems, 2001, 26(5):383-423.
[11] Pedersen T, Tryfona N. Pre-aggregation in spatial data warehouses[C]. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases. Berlin: Springer-Verlag, 2001: 460-478.
[12] Camossi E, Bertolotto M, Bertino E, Guerrini G. A multigranular spatiotemporal data model[C]. Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems. New Orleans: ACM Press, 2003: 94-101.
[13] Bimonte S, Tchounikine A, Miquel M. Towards a spatial multidimensional model[C]. Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2005: 39–46.
[14] Jensen C, Klygis A, Pedersen T, Timko I. Multidimensional data modeling for location-based services[J]. VLDB Journal, 2004, 13(1):1–21.
[15] Malinowski E, Zimanyi E. Representing spatiality in a conceptual multidimensional model[C]. Proceedings of the 12th annual ACM International workshop on Geographic Information Systems. New York: ACM Press, 2004: 12–22.
[16] Malinowski E, Zimanyi E. Spatial Hierarchies and Topological Relationships in the Spatial MultiDimER model[C]. Proceedings of the 22th International Conference on Databases. British: Springer-Verlag, 2005: 17–28.
[17] Damiani M L, Stefano Spaccapietra. Spatial Data Warehouse Modelling[J]. Processing and Managing Complex Data for Decision Support. 2006: 1~27.
[18]周成虎,李军.地球空间元数据研究[J].地球科学, 2000, 25(6): 579-584.
[19]王红兵.数据仓库中的元数据[J].微机发展, 1995{5}: 44-48.
[20]汪小林,罗英伟,丛升日等.空间元数据研究及应用[J].计算机研究与发展, 2001, 38(3): 321-327.
[21]段怡红. ISO/TC211关于元数据的研究工作[J].测绘标准化, 1998, 14(l): 2-5.
[22]中国21世纪议程管理中心.中国地理信息元数据标准研究[M].北京:科学出版社, 1999.
[23]李军,周成虎.地球空间数据元数据标准初探[J].地理科学进展, 1998, 17(4): 55-63.
[24] FGDC. content standars for digitalgeo-spatial metadara. Federal Geogasphic Data Committee, 1997, 6.
[25] FGDC. http://www.fgde.govlclearinghouse. 1998.
[26] Cen/TC287 Seeretariar. CEN/TC287 Geographic Infomation. http: //www. statkat. No / sk/standard/cen, 1996.
[27] ISO/TC211. Geographic information-metadata. ISO standard 15046-15 Metadata, version2.0, 1997.
[28] ISO/TC211 WG3. ISO Standard 15046-15 Geographic lnformation-Metadata, V4.4. 1998.
[29] ISO/TC211 Metadata. Working Document: Geographic Information-15046 Part 15 Matadata, 1997-12-22.
[30] Stefanovic N, Han J W. Object - based selective materialization for efficient implementation of spatial data cubes[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(6): 1-21.
[31] Shekhar S, Tan X. Map Cube: A Visualization Tool for Spatial Data Warehouses[J/OL]. http :/ / www. cs. umn. edu/ research/ shashi -group/, 2005.
[32] Zhou X F, Han J W. Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining[C]. Proceedings of the 6th International Conference on SSD. USA, 1999: 121-125.
[33] Papadias D. Efficient OLAP Operations in Spatial Data Warehouses[R]. HongKong: Technical Report, 2001: 65-69.
[34] Kamp V, Sitzmann L. A Spatial Data Cube Concept to Support Data Analysis in Environmental Epidemiology[C]. Proceedings of the 9th International Conference on SSDBM. Canada, 1997: 234-238.
[35] Wang B Y, Pan F, Ren D, et al. Efficient OLAP Operations for Spatial Data Using Peano Trees[C]. Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. San Diego: ACM Press, 2003: 28-34.
[36] Prasher S, Zhou X F. Multi-Resolution Amalgamation: Dynamic Spatial Data Cube Generation[C]. Proceedings of the 15th Australasian Database Conference. Dunedin, 2004: 345-349.
[37] Rauber A, Tomsick P, Riedel H, et al. Integration Geo-Spatial Data into OLAP Systems Using a Set - Based Quad-Tree Representation[C]. Proceedings of the 9th International Conference on SSDBM. Canada, 1997: 256-260.
[38]邹逸江,李德仁,王任享.空间数据立方体分析操作原理[J].武汉大学学报:信息科学版, 2004, 29(9): 822-826.
[39]邹逸江.多维空间分析的关键技术-空间数据立方体[J].地理与地理信息科学, 2006, 22(1): 12-16.
[40]邹逸江.空间数据立方体的多维数据组织及存储[J].地理与地理信息科学, 2006, 22(5): 26-30.
[41]邹逸江,杨晓平.空间数据立方体的技术框架[J].计算机应用研究, 2006(10): 27-29.
[42]邹逸江.空间数据立方体的空间度量聚集[J].计算机应用研究, 2007, 24(7): 13-15.
[43]邹逸江.空间数据立方体多维信息空间分析实例[J].计算机应用研究, 2007(2): 199-202.
[44]邹逸江.面向数字城市多源数据的空间数据立方体分析研究[J].计算机应用研究, 2008, 25(2): 043-143.
[45]邹逸江.面向数字城市多源数据的空间数据立方体数据组织研究[J].计算机应用研究, 2008, 25(5): 2641-3641.
[46]樊博,李一军.空间数据立方体的物化视图选择方法研究[J].高技术通讯, 2003, 3(1): 15-21.
[47]李一军,樊博.空间OLAP技术研究[J].管理科学学报, 2003, 6(4): 9-16.
[48]樊博.空间OLAP的计算方法[J].系统工程理论与实践, 2007(11):87-96.
[49]樊博,李海刚,孟庆国.空间数据立方体的建模方法研究[J].计算机工程, 2007, 33(8): 1-3.
[50]樊博,李海刚,孟庆国,李一军.一种时空OLAP的索引技术研究[J].计算机应用研究, 2007(2): 54-56.
[51]徐铭杰,李慧典,王淑彬.基于Web的空间OLAP研究[J].地域研究与开发, 2002, 21(3): 89-92.
[52]徐铭杰,梁留科.空间多维数据模型及OLAP的设计与实现[J].测绘学院学报, 2002, 19(2): 124-127.
[53]胡孔法,董逸生,陈崚.空间数据仓库与OLAP技术研究[C].数字江苏论坛--电子政务与地理信息技术论文专辑, 2005: 7-9
[54] Padadias D, Kalnis P, Zhang J, Tao Y. Efficient OLAP Operations in Spatial Data Warehouses[C]. Proceedings of the 7th International Symposium on Advances in Soatial and Temporal Databases. London: Springer-Verlag, 2001: 443-459.
[55] Jurgens M, Lenz H J. The aR-tree: An Improved R-tree with Materialized Data for Supporting Range Queries on OLAP-Data[C]. Proceedings of the 9th International Workshop on Database and Expert Systems Applications. Washington: IEEE Computer Society, 1998: 186-191.
[56] Lazaridis I, Mehrotra S. Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure[C]. Proceedings of the ACM SIGMOD International Conference on Management of data. New York: ACM Press, 2001: 401-412.
[57] Shashi Shekhar, Sanjay Chawla.空间数据库[M].谢昆青,马修军,杨冬青等译.北京:机械工业出版社, 2004.
[58] Komacker M, Mohan C, Hellerstein J M. Concurrency and Recovery in Generalized Search Trees[J]. ACM SIGMOD Record, 1997, 26(2): 62-72.
[59] Schreck T, Chen Z. Branch Grafting Method for R-tree Implementation[J]. The Journal of Systems and Software, 2000, 53: 83-93.
[60]张明波,陆锋,申排伟,程昌秀. R树家族的演变和发展[J].计算机学报, 2005, 28(3): 289-300.
[61]郭龙江,李建中.空间数据库的索引技术[J].黑龙江大学自然科学学报, 2005, 22(3): 288-293.
[62] Hong S, Song B, Lee S. Efficient Execution of Range-Aggregate Queries in Data Warehouse Environments[C]. Proceedings of the 20th International Conference on Conceptual Modeling. Yokohama: Springer-Verlag, 2001: 299-310.
[63]陈细谦,王占昌,曹秀坤,迟忠先.一种有效的空间数据仓库区域聚集查询索引结构[J].计算机研究与发展,2006,43(1):75-80.
[64] Papadias D, Tao Y F, Kalnis P, Zhang J. Indexing Spatio-temporal Data Warehouses[C]. Proceedings of the 18th International Conference on Data Engineering. Washington: IEEE Computer Society, 2002: 166-175.
[65] Yang J, Widom J. Incremental Computation and Maintenance of Temporal Aggregates[J]. The VLDB Journal, 2003, 12(3): 262-283.
[66] Zhang D, Markowetz A, Tsotras V, et al. Efficient Computation of Temporal Aggregates with Range Predicates[C]. Proceedings of the 20th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. New York: ACM Press, 2001: 237-245.
[67] Zhang D, Tsotras V J, Gunopulos D. Efficient Aggregation over Objects with Extent[C]. Proceedings of the 21th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. New York: ACM Press, 2002: 121-132.
[68] Govindarajan S, Agarwal P K, Arge L. CRB-Tree: An Efficient Indexing Scheme for Range Aggregate Queries[C]. Proceedings of the 9th International Conference on Database Theory. London: Springer-Verlag, 2003: 143-157.
[69] Tao Y, Papadias D. Range Aggregate Processing in Spatial Databases[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1555-1570.
[70] Muralikrishna M, Dewitt D. Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. Chicago: ACM Press, SIGMOD, 1988: 28-36.
[71] Poosala Y, Ioannidis Y. Selectivity Estimation without the Attribute Value Independence Assumption[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Greece: Morgan Kaufmann Publishers, 1997: 486-495.
[72] Acharya S, Poosala Y, Ramaswamy S. Selectivity Estimation in Spatial Databases[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999: 13-24.
[73] Gunopulos D, Kollios G, Tsotras V, Domeniconi C. Approximate Multi-Dimensional Aggregate Range Queries over Real Attributes[J]. ACM SIGMOD Record, 2000, 29(2): 463-474.
[74] Stefanovic N, Han J W, Kooerski K. Object-based Selective Materialization for Efficient Implementation of Spatial Data Cubes[J] . IEEE Transactions on Knowledge and Data Engineering, 2004, 12(6): 938-958.
[75] Stefanovic N. Design and Implementation of On-line Analytical Processing (OLAP) of Spatial Data[D]. Canada: Simon Fraser University, 1997.
[76]万鲁河,刘万宇,崔金香.基于模拟退火算法的空间度量物化选择[J].哈尔滨工业大学学报, 2008, 40(7): 0011-2011.
[77] Li J J, Wang Y, Liu R Q. Selection of Materialized View Based on Information Weight and Using Huffman-Tree on Spatial Data Warehouse[C]. Proceeding of the 1th International Conference on Innovative Computing, Information and Control. Beijing: IEEE Computer Society, 2006: 71-74.
[78] Yu S M, Vijayalakshmi A, Nabil A. Selective View Materialization in a Spatial Data Warehouse[C]. Proceeding of the 7th International Conference on Data Warehousing and Knowledge Discovery. Copenhagen: Springer-Verlag, 2005: 157-167.
[79] Yu S M, Atluri V, Adam N. Preview: Optimizing View Materialization Cost in Spatial Data Warehouses[C]. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery. Heidelberg: Springer-Verlag, 2006: 45–54.
[80] Guttman A. R-Tree: A Dynamic Index Structure for Spatial Searching[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1984: 47-57.
[81] Sellis T, Roussopoulos N, Faloutsos C. The R+-Tree: A Dynamic Index for Multi - Dimensional Objects[C]. Proceedings of the 13th. International Conference on Very Large Data Bases. Brighton: Morgan Kaufmann Publishers, 1987: 507-518.
[82] Beckmann N, Begel H P, Schneide R, Seeger B. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1990: 322-331.
[83] Seeger B, Kriegel H P. The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems[C]. Proceedings of the 16th International Conference on Very Large Data Bases. Australia: Morgan Kaufmann Publishers, 1990: 590-601.
[84] Datta A, Moon B, Thomas H. A Case for Parallelism in Data Warehousing and OLAP[C]. Proceedings of the 9th International Workshop on Database and Expert Systems Applications. Washington: IEEE Computer Society, 1998: 226-231.
[85] Stohr T, Martens H, Rahm E. Multi-Dimensional Database Allocation for Parallel Data Warehouses[C]. Proceedings of the 26th International Conference on Very Large Data Bases. Egypt: Morgan Kaufmann Publishers, 2000: 273-284.
[86] Dehne F, Eavis T, Hambrusch S, et al. Parallelizing the Data Cube[C]. Proceedings of the 8th International Conference on ICDT. London: Springer-Verlag, 2001: 129-143.
[87]骆吉洲,李建中,赵锴.大型压缩数据仓库上的Iceberg Cube算法[J].软件学报, 2006, 17(8): 1743-1752.
[88] Gao H, Li J Z. Cube Algorithms for Very Large Data Warehouses[J]. Journal of Software, 2001,12(6): 830-839.
[89] Desshpande P M, Naughton J F. An Array-based Algorithm for Simultaneous Multidimensional Aggregations[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1997: 159-170.
[90] Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: Shrinking the Peta Cube[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2002: 464-475.
[91] Kong Y F, Chen H. GFSC—The storage of free cube based on graph structure[J]. Journal of Computer Research and Development, 2004, 41(10): 1652-1660.
[92] Rotem D, Srivastava J. Aggregation Algorithms for Very Large Compressed Data Warehouses[C]. Proceedings of the 25th International Conference on Very Large Data Bases. Mumbai: Morgan Kaufmann Publishers, 1999: 651-662.
[93] Poosala V. Histogram-based Estimation Techniques in Database[D]. University of Wisconsin at Madison, 1997.
[94] Aboulnaga A, Chaudhuri S. Self-Tuning Histograms: Building Histograms without Looking at Data[C]. Proceedings ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999:181-192.
[95] Gunopulos D, Kollios G, V. Tsotras J, Domeniconi C. Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2000: 463-474.
[96] Bruno N, Chaudhuri S, Gravano L. STHoles: A Multidimensional Workload-Aware Histogram[J]. ACM SIGMOD Record, 2001, 30(2): 211-222.
[97] Deshpande A, Garofalakis M, Rastogi R. Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2001: 199-210.
[98]李盛恩,王珊. Star Cube----一种高效的数据立方体实现方法[J].计算机研究与发展, 2004, 41(4): 587-593.
[99] Li S E, Wang S. Research on Closed Data Cube Technology[J]. Journal of Software, 2004, 15(8): 1165-1171.
[100]吕晓华,翁伟,贾宇波,段江娇.高效快速计算FreeCube的SPT算法[J].计算机工程与设计, 2006, 27(14): 2597-2600.
[101] Wang W, Lu H J, Feng J L, Yu J X. Condensed cube: An Effective Approach to Reducing Data Cube Size[C]. Proceedings of the 18th International Conference on Data Engineering. Los Angeles: IEEE Computer Society Press, 2002: 155-165.
[102] Sismanis Y, Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: Shrinking the PetaCube [C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2002: 464-475.
[103] Lakeshmanan L V S, Pei J, Han J W. Quotient Cubes: How to Summarize the Semantics of a Data Cube[C]. Proceedings of the 28th International Conference on Very Large Data Bases. San Fransisco: Morgan Kaufmann Publishers, 2002: 476-487.
[104] Beyer K S, Ramakrishan R. Bottom-Up Computation of Sparse and Iceberg Cubes[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999: 359-370.
[105] Han J W, Pei J, Dong GZ, Wang K. Efficient Computation of Iceberg Cubes with Complex Measures[C]. Proceedings of the ACM SIGMOD Conference. New York: ACM Press, 2001: 1-12.
[106]侯东风,陆昌辉,刘青宝,张维明.数据立方体计算方法研究综述[J].计算机科学, 2008, 35(10): 1-5.
[107] Shukla A, Deshpande P, Naughton J F. Materialized View Selection for Multidimensional Datasets[C]. Proceedings of the 24th International Conference on Very Large Data Bases. New York: Morgan Kaufmann Publishers, 1998: 488-499.
[108] Baralis E, Paraboschi S, Teniente E. Materialized View Selection in a Multidimensional Database[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Athens: Morgan Kaufmann Publishers, 1997:156-165.
[109] Yang J, Karlaonlen K, Li Q. Algorithm for Materialized View Design in Data Warehousing Environment[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Athens: Morgan Kaufmann Publishers, 1997: 136-145.
[110] Zhang C, Yang J. Genetic Algorithm for Materialized View Selection in Data Warehouse Environments[C]. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery. Florence: Springer-Verlag, 1999: 116-125.
[111] Gupta H, Mumick I S. Selection of Views to Materialize in a Data Warehouse[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(1): 24-43.
[112]冷春霞,数据仓库中物化视图的选择方法[J].华东理工大学学报:自然科学版, 2005, 31(2): 203-207.
[113]顾军华,赵秀丽,谭庆.蚁群算法在物化视图选择问题中的应用[J].计算机应用, 2007,27(11): 2763-2765.
[114]王伟皓,郑宁.物化视图选择中权限因子的设计[J].计算机应用与软件, 2007, 24(10): 104-106.
[115]王自强,孙霞,张德贤.数据仓库中用于视图选择的增强遗传算法[J].小型微型计算机系统, 2007, 28(2): 267-371.
[116] Yan Y, Wang P, Wang C, et al. Anne: An Efficient Framework on View Selection Problem[C]. Proceedings of the 6th Asia-Pacific Web Conference on Advanced Web Technologies and Applications. Hangzhou: Springer-Verlag, 2004: 384-394.
[117] Deshpande P, Ramaswamy K, Shukla A, Naughton J. Caching Multidimensional Queries Using Chunks[C]. Proceedings of ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998: 259-270.
[118] Choi C H, Yu J X, Lu H J. Dynamic Materialized View Management based on Predicates[C]. Proceedings of the 5th Asia-Pracific Web Conference on Web Technologies and Applications. Xi’an: Springer-Verlag, 2003: 583-594.
[119]张柏礼,孙志挥,周晓云,张净. DMVR:一种基于Cache的动态物化视图置换算法[J].应用科学学报, 2006, 24(2): 159-164.
[120] Aouiche K, Jouve P E, Darmont J. Clustering-Based Materialized View Selection in Data Warehouses[C]. Proceedings of the 10th East European Conference on Advances in Databases and Information Systems. Saloniki: Springer-Verlag, 2006: 81-95.
[121]薛永生,林子雨,段江娇,吕晓华,张伟.用多用户多窗口方法处理多维视图动态选择[J].计算机研究与发展, 2004, 41(10): 1703-1711.
[122]张东站,黄宗毅,薛永生. NDSMMV—种多维数据集物化视图动态选择新策略[J].计算机研究与发展, 2008, 45(5): 901-908.
[123]张柏礼,孙志挥,周晓云,杨宜东,朱玉全.静态物化视图的动态Cache优化算法[J].软件学报, 2006, 17(5): 1213-1221.
[124]袁贞明.基于样例的空间数据检索技术研究[D].杭州:浙江大学, 2005.
[125] Worboys M. GIS: A computing perspective[M]. London: Taylor and Francis, 1995.
[126]马智民,俞全宏,姜作勤.应用地理信息系统设计与实现[M].西安:西安地图出版社, 1996.
[127] Gaede V, Gunther O. Multimensional Access Methods[J]. ACM Computing Surveys, 1998, 30(2): 170-231.
[128] Liang Y, Zhang H. An Algorithm for Multi-Way Distance Join Query[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Taipei: IEEE Press, 2006: 412-417.
[129]梁银,张虹.一种多路空间距离连接查询处理方法[J].计算机应用, 2008, 28(1): 155-158.
[130] Gray J, Chaudhuri S, Bosworth A, et al. Data Cube: a Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub-Totals[J]. Data Mining and Knowledge Discovery, 1997: 29-53.
[131] Shekhar S, Chawla S. Spatial Databases: A Tour[M]. Prentice Hall: New Jersey, 2003.
[132] Liang Y, Zhang H. Design and Implementation of Enterprise Spatial Data Warehouse[C]. Proceedings of IFIP Conference. Beijing: Springer-Verlag, 2007: 75-83.
[133]梁银,张虹.企业空间数据仓库的构建方法[J].山东大学学报:理学版. 2007, 42(9): 51-55.
[134] Zhang L, Li Y, Rao F, et al. An Approach to Enabling Spatial OLAP by Aggregating on Spatial Hierarchy[C]. Proceedings of DaWak Conference. Heidelberg: Springer-Verlag, 2003: 35-44.
[135] Rao F, Zhang L, Yu X L, et al. Spatial Hierarchy and OLAP-favored Search in Spatial Data Warehouse[C]. Proceedings of the 6th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2003: 48-55.
[136] Gupta H, Mumick I S. Selection of Views to Materialize under a Maintenance Cost Constraint[C]. Proceedings of 8th International Conference of Data Theory. Heidelberg: Springer-Verlag, 1999: 453-470.
[137] Jeffrey X Y, Yao X, et al. Materialized View Selection as Constrained Evolutionary Optimization[J]. IEEE Transactions on Systems, Man and Cybernetics, 2003, 33(4): 458-467.
[138] Zhang C, Yao X, Yang J. An Evolutionary Approach to Materialized Views Selection in a Data Warehouse Environment[J]. IEEE Transactions on Systems, Man and Cybernetics, 2001, 31(3): 282-294.
[139]梁银,张虹.空间区域聚集查询方法研究[J].计算机工程与应用, 2009, 45(25): 135-137.
[140] Harinarayan V, Rajaraman A, Ullman J D. Implementing Data Cubes Efficiently[C]. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1996: 205-216.
[141] Nadeau T P, Teorey T J. Achieving Scalability in OLAP Materialized View Selection[C]. Proceedings of the 5th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2002: 28-34.
[142]童云海,谢昆青,唐世渭.空间Cube计算方法[J].计算机工程与应用, 2002(6): 1-5.
[143]童云海,谢昆青,唐世渭.空间数据仓库模型和空间Cube计算方法[J].计算机科学, 2002, 29(10): 1-5.
[144] Han J , Kamber M. Data Mining: Concepts and Techniques[M] . Morgan Kaufmann Publishers , 2000.
[145] Wilkin G A, Huang X Z. K-Means Clustering Algorithms: Implementation and Comparison[C]. Proceedings of the 2th International Multi-Symposium of Computer and Computational Sciences. Iowa: IEEE Press, 2007: 133-136.
[146] Ordonez C, Omiecinski E. FREM: Fast and robust EM clustering for large data sets[C]. Proceedings of ACM CIKM International Conference on Information and Knowledge Management. McLean: ACM Press, 2002: 590-599.
[147] Kaufman L, Rousseeuw P J. Finding Groups in Data: an Introduction to Cluster Analysis[M]. John Wiley & Sons, 1990.
[148] Ng R T, Han J W. Effencient and Effective Clustering Methods for Spatial Data Mining[C]. Proceedings of the 20th International Conference on Very Large Data Bases. Santiago: VLDB Press, 1994: 144-155.
[149] Zhang T, Ramakrishnan R, Livny M. BIRCH: An Efficient Data Clustering Method for Very Large Databases[C]. Proceedings of ACM SIGMOD International Conference on Management of Data. Montreal: ACM Press, 1996: 103-114.
[150] Karypis G, Han E H, Kumar V. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling[J]. IEEE Computer, 1999, 32(8): 68-75.
[151] Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithm for Large Databases[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998: 73-84.
[152] Guha S, Rastogi R, Shim K. ROCK: A Robust Clustering Algorithm for Categorical Attributes[C]. Proceedings of 15th. International Conference on Data Engineering. Australia: ICDE press, 1999: 512-521.
[153] Hinneburg A, Keim D A. An Efficient Approach to Clustering in Large Multimedia Databases with Noise[C]. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining. New York: AAAI Press, 1998: 58-65.
[154] Ankerst M, Breuning M M, Kriegel H P, Sander J. OPTICS: Ordering Points to Identify the Clustering Structure[C]. Proceedings of ACM SIGMOD Internetional Conference on Management of Data. Philadelphia: ACM Press, 1999: 49-60.
[155] Ester M, Kriegel H P, Sander J, Xu X W. A Density - based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]. Proceedings of the 2th International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231.
[156] Wang W, Yang J, Muntz R. STING: A Statistical Information Grid Approach to Spatial Data Mining[C]. Proceedings of the 23th VLDB Conference. Athens, 1997: 186-195.
[157] Sheikholeslami G, Chatterjee S, Zhang A D. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases[C]. Proceedings of the 24th VLDB Conference. New York, 1998: 428-439.
[158] Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic Subspace Cclustering of High Dimensional Data for Data Mining Applications[J]. ACM SIGMOD Record, 1998, 27(2): 94-105.
[159]杨风召.高维数据挖掘技术研究[M].南京:东南大学出版社, 2007.
[160]林子雨,杨冬青,宋国杰.实时主动数据仓库中多维数据实视图的选择[J].软件学报, 2008, 19(2): 301-313.
[161] Theodoridis Y, Stefanakis E, Sellis T. Efficient Cost Model for Spatial Queries Using R-tree[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(1): 1-21.
[162] Kalnis P, Mamoulis N, Papadias D. View Selection Using Randomized Search[J]. Data & Knowledge Engineering, 2002, 42(1): 89-111.
[163]谭红星,周龙骧.多维数据实视图的动态选择[J].软件学报, 2002, 13(6): 1090– 1096.
[164]梁银,张虹.一种空间数据仓库动态物化视图选择算法[J].计算机工程与应用, 2008, 44(28): 122-124.
[2]陈细谦.空间数据仓库关键技术的研究与实现[D].大连:大连理工大学, 2005.
[3]陈细谦,迟忠先,昃宗亮,苏立强.地理编码在空间数据仓库ETL中的应用[J].小型微型计算机系统, 2005, 26(4): 628-630.
[4]陈细谦,迟忠先,金妮.城市地理编码系统应用与研究[J].计算机工程, 2004, 30(23): 50-52.
[5]张宁,贾自艳,史忠植.数据仓库中ETL技术的研究[J].计算机工程与应用, 2002, 38(24): 213-216.
[6]田扬戈,边馥苓.空间数据仓库的ETL研究[J].武汉大学学报:信息科学版, 2007, 31(4): 362-365.
[7] Han J W, Stefanovic N, Kopersky K. Selective materialization: an Efficient Method for Spatial Data Cube Construction[C]. Proceedings of Research and Development in Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 1998: 144-158.
[8] Rivest S, Bedard Y, Marchand P. Towards Better Support for Spatial Decision Making: Defining the Characteristics of Spatial on-line Analytical Processing (SOLAP)[J]. Geomatica, 2001, 55(4): 539-555.
[9] Fidalgo R N, Times V C, Silva J, Souza F. GeoDWFrame: A framework for guiding the design of geographical dimensional schemas[C]. Proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery. Berlin: Springer-Verlag, 2004: 26-37.
[10] Pedersen T, Jensen C, Dyreson C. A foundation for capturing and querying complex multidimensional data[J]. Information Systems, 2001, 26(5):383-423.
[11] Pedersen T, Tryfona N. Pre-aggregation in spatial data warehouses[C]. Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases. Berlin: Springer-Verlag, 2001: 460-478.
[12] Camossi E, Bertolotto M, Bertino E, Guerrini G. A multigranular spatiotemporal data model[C]. Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems. New Orleans: ACM Press, 2003: 94-101.
[13] Bimonte S, Tchounikine A, Miquel M. Towards a spatial multidimensional model[C]. Proceedings of the 8th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2005: 39–46.
[14] Jensen C, Klygis A, Pedersen T, Timko I. Multidimensional data modeling for location-based services[J]. VLDB Journal, 2004, 13(1):1–21.
[15] Malinowski E, Zimanyi E. Representing spatiality in a conceptual multidimensional model[C]. Proceedings of the 12th annual ACM International workshop on Geographic Information Systems. New York: ACM Press, 2004: 12–22.
[16] Malinowski E, Zimanyi E. Spatial Hierarchies and Topological Relationships in the Spatial MultiDimER model[C]. Proceedings of the 22th International Conference on Databases. British: Springer-Verlag, 2005: 17–28.
[17] Damiani M L, Stefano Spaccapietra. Spatial Data Warehouse Modelling[J]. Processing and Managing Complex Data for Decision Support. 2006: 1~27.
[18]周成虎,李军.地球空间元数据研究[J].地球科学, 2000, 25(6): 579-584.
[19]王红兵.数据仓库中的元数据[J].微机发展, 1995{5}: 44-48.
[20]汪小林,罗英伟,丛升日等.空间元数据研究及应用[J].计算机研究与发展, 2001, 38(3): 321-327.
[21]段怡红. ISO/TC211关于元数据的研究工作[J].测绘标准化, 1998, 14(l): 2-5.
[22]中国21世纪议程管理中心.中国地理信息元数据标准研究[M].北京:科学出版社, 1999.
[23]李军,周成虎.地球空间数据元数据标准初探[J].地理科学进展, 1998, 17(4): 55-63.
[24] FGDC. content standars for digitalgeo-spatial metadara. Federal Geogasphic Data Committee, 1997, 6.
[25] FGDC. http://www.fgde.govlclearinghouse. 1998.
[26] Cen/TC287 Seeretariar. CEN/TC287 Geographic Infomation. http: //www. statkat. No / sk/standard/cen, 1996.
[27] ISO/TC211. Geographic information-metadata. ISO standard 15046-15 Metadata, version2.0, 1997.
[28] ISO/TC211 WG3. ISO Standard 15046-15 Geographic lnformation-Metadata, V4.4. 1998.
[29] ISO/TC211 Metadata. Working Document: Geographic Information-15046 Part 15 Matadata, 1997-12-22.
[30] Stefanovic N, Han J W. Object - based selective materialization for efficient implementation of spatial data cubes[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(6): 1-21.
[31] Shekhar S, Tan X. Map Cube: A Visualization Tool for Spatial Data Warehouses[J/OL]. http :/ / www. cs. umn. edu/ research/ shashi -group/, 2005.
[32] Zhou X F, Han J W. Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining[C]. Proceedings of the 6th International Conference on SSD. USA, 1999: 121-125.
[33] Papadias D. Efficient OLAP Operations in Spatial Data Warehouses[R]. HongKong: Technical Report, 2001: 65-69.
[34] Kamp V, Sitzmann L. A Spatial Data Cube Concept to Support Data Analysis in Environmental Epidemiology[C]. Proceedings of the 9th International Conference on SSDBM. Canada, 1997: 234-238.
[35] Wang B Y, Pan F, Ren D, et al. Efficient OLAP Operations for Spatial Data Using Peano Trees[C]. Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. San Diego: ACM Press, 2003: 28-34.
[36] Prasher S, Zhou X F. Multi-Resolution Amalgamation: Dynamic Spatial Data Cube Generation[C]. Proceedings of the 15th Australasian Database Conference. Dunedin, 2004: 345-349.
[37] Rauber A, Tomsick P, Riedel H, et al. Integration Geo-Spatial Data into OLAP Systems Using a Set - Based Quad-Tree Representation[C]. Proceedings of the 9th International Conference on SSDBM. Canada, 1997: 256-260.
[38]邹逸江,李德仁,王任享.空间数据立方体分析操作原理[J].武汉大学学报:信息科学版, 2004, 29(9): 822-826.
[39]邹逸江.多维空间分析的关键技术-空间数据立方体[J].地理与地理信息科学, 2006, 22(1): 12-16.
[40]邹逸江.空间数据立方体的多维数据组织及存储[J].地理与地理信息科学, 2006, 22(5): 26-30.
[41]邹逸江,杨晓平.空间数据立方体的技术框架[J].计算机应用研究, 2006(10): 27-29.
[42]邹逸江.空间数据立方体的空间度量聚集[J].计算机应用研究, 2007, 24(7): 13-15.
[43]邹逸江.空间数据立方体多维信息空间分析实例[J].计算机应用研究, 2007(2): 199-202.
[44]邹逸江.面向数字城市多源数据的空间数据立方体分析研究[J].计算机应用研究, 2008, 25(2): 043-143.
[45]邹逸江.面向数字城市多源数据的空间数据立方体数据组织研究[J].计算机应用研究, 2008, 25(5): 2641-3641.
[46]樊博,李一军.空间数据立方体的物化视图选择方法研究[J].高技术通讯, 2003, 3(1): 15-21.
[47]李一军,樊博.空间OLAP技术研究[J].管理科学学报, 2003, 6(4): 9-16.
[48]樊博.空间OLAP的计算方法[J].系统工程理论与实践, 2007(11):87-96.
[49]樊博,李海刚,孟庆国.空间数据立方体的建模方法研究[J].计算机工程, 2007, 33(8): 1-3.
[50]樊博,李海刚,孟庆国,李一军.一种时空OLAP的索引技术研究[J].计算机应用研究, 2007(2): 54-56.
[51]徐铭杰,李慧典,王淑彬.基于Web的空间OLAP研究[J].地域研究与开发, 2002, 21(3): 89-92.
[52]徐铭杰,梁留科.空间多维数据模型及OLAP的设计与实现[J].测绘学院学报, 2002, 19(2): 124-127.
[53]胡孔法,董逸生,陈崚.空间数据仓库与OLAP技术研究[C].数字江苏论坛--电子政务与地理信息技术论文专辑, 2005: 7-9
[54] Padadias D, Kalnis P, Zhang J, Tao Y. Efficient OLAP Operations in Spatial Data Warehouses[C]. Proceedings of the 7th International Symposium on Advances in Soatial and Temporal Databases. London: Springer-Verlag, 2001: 443-459.
[55] Jurgens M, Lenz H J. The aR-tree: An Improved R-tree with Materialized Data for Supporting Range Queries on OLAP-Data[C]. Proceedings of the 9th International Workshop on Database and Expert Systems Applications. Washington: IEEE Computer Society, 1998: 186-191.
[56] Lazaridis I, Mehrotra S. Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure[C]. Proceedings of the ACM SIGMOD International Conference on Management of data. New York: ACM Press, 2001: 401-412.
[57] Shashi Shekhar, Sanjay Chawla.空间数据库[M].谢昆青,马修军,杨冬青等译.北京:机械工业出版社, 2004.
[58] Komacker M, Mohan C, Hellerstein J M. Concurrency and Recovery in Generalized Search Trees[J]. ACM SIGMOD Record, 1997, 26(2): 62-72.
[59] Schreck T, Chen Z. Branch Grafting Method for R-tree Implementation[J]. The Journal of Systems and Software, 2000, 53: 83-93.
[60]张明波,陆锋,申排伟,程昌秀. R树家族的演变和发展[J].计算机学报, 2005, 28(3): 289-300.
[61]郭龙江,李建中.空间数据库的索引技术[J].黑龙江大学自然科学学报, 2005, 22(3): 288-293.
[62] Hong S, Song B, Lee S. Efficient Execution of Range-Aggregate Queries in Data Warehouse Environments[C]. Proceedings of the 20th International Conference on Conceptual Modeling. Yokohama: Springer-Verlag, 2001: 299-310.
[63]陈细谦,王占昌,曹秀坤,迟忠先.一种有效的空间数据仓库区域聚集查询索引结构[J].计算机研究与发展,2006,43(1):75-80.
[64] Papadias D, Tao Y F, Kalnis P, Zhang J. Indexing Spatio-temporal Data Warehouses[C]. Proceedings of the 18th International Conference on Data Engineering. Washington: IEEE Computer Society, 2002: 166-175.
[65] Yang J, Widom J. Incremental Computation and Maintenance of Temporal Aggregates[J]. The VLDB Journal, 2003, 12(3): 262-283.
[66] Zhang D, Markowetz A, Tsotras V, et al. Efficient Computation of Temporal Aggregates with Range Predicates[C]. Proceedings of the 20th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. New York: ACM Press, 2001: 237-245.
[67] Zhang D, Tsotras V J, Gunopulos D. Efficient Aggregation over Objects with Extent[C]. Proceedings of the 21th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. New York: ACM Press, 2002: 121-132.
[68] Govindarajan S, Agarwal P K, Arge L. CRB-Tree: An Efficient Indexing Scheme for Range Aggregate Queries[C]. Proceedings of the 9th International Conference on Database Theory. London: Springer-Verlag, 2003: 143-157.
[69] Tao Y, Papadias D. Range Aggregate Processing in Spatial Databases[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1555-1570.
[70] Muralikrishna M, Dewitt D. Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. Chicago: ACM Press, SIGMOD, 1988: 28-36.
[71] Poosala Y, Ioannidis Y. Selectivity Estimation without the Attribute Value Independence Assumption[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Greece: Morgan Kaufmann Publishers, 1997: 486-495.
[72] Acharya S, Poosala Y, Ramaswamy S. Selectivity Estimation in Spatial Databases[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999: 13-24.
[73] Gunopulos D, Kollios G, Tsotras V, Domeniconi C. Approximate Multi-Dimensional Aggregate Range Queries over Real Attributes[J]. ACM SIGMOD Record, 2000, 29(2): 463-474.
[74] Stefanovic N, Han J W, Kooerski K. Object-based Selective Materialization for Efficient Implementation of Spatial Data Cubes[J] . IEEE Transactions on Knowledge and Data Engineering, 2004, 12(6): 938-958.
[75] Stefanovic N. Design and Implementation of On-line Analytical Processing (OLAP) of Spatial Data[D]. Canada: Simon Fraser University, 1997.
[76]万鲁河,刘万宇,崔金香.基于模拟退火算法的空间度量物化选择[J].哈尔滨工业大学学报, 2008, 40(7): 0011-2011.
[77] Li J J, Wang Y, Liu R Q. Selection of Materialized View Based on Information Weight and Using Huffman-Tree on Spatial Data Warehouse[C]. Proceeding of the 1th International Conference on Innovative Computing, Information and Control. Beijing: IEEE Computer Society, 2006: 71-74.
[78] Yu S M, Vijayalakshmi A, Nabil A. Selective View Materialization in a Spatial Data Warehouse[C]. Proceeding of the 7th International Conference on Data Warehousing and Knowledge Discovery. Copenhagen: Springer-Verlag, 2005: 157-167.
[79] Yu S M, Atluri V, Adam N. Preview: Optimizing View Materialization Cost in Spatial Data Warehouses[C]. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery. Heidelberg: Springer-Verlag, 2006: 45–54.
[80] Guttman A. R-Tree: A Dynamic Index Structure for Spatial Searching[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1984: 47-57.
[81] Sellis T, Roussopoulos N, Faloutsos C. The R+-Tree: A Dynamic Index for Multi - Dimensional Objects[C]. Proceedings of the 13th. International Conference on Very Large Data Bases. Brighton: Morgan Kaufmann Publishers, 1987: 507-518.
[82] Beckmann N, Begel H P, Schneide R, Seeger B. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1990: 322-331.
[83] Seeger B, Kriegel H P. The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems[C]. Proceedings of the 16th International Conference on Very Large Data Bases. Australia: Morgan Kaufmann Publishers, 1990: 590-601.
[84] Datta A, Moon B, Thomas H. A Case for Parallelism in Data Warehousing and OLAP[C]. Proceedings of the 9th International Workshop on Database and Expert Systems Applications. Washington: IEEE Computer Society, 1998: 226-231.
[85] Stohr T, Martens H, Rahm E. Multi-Dimensional Database Allocation for Parallel Data Warehouses[C]. Proceedings of the 26th International Conference on Very Large Data Bases. Egypt: Morgan Kaufmann Publishers, 2000: 273-284.
[86] Dehne F, Eavis T, Hambrusch S, et al. Parallelizing the Data Cube[C]. Proceedings of the 8th International Conference on ICDT. London: Springer-Verlag, 2001: 129-143.
[87]骆吉洲,李建中,赵锴.大型压缩数据仓库上的Iceberg Cube算法[J].软件学报, 2006, 17(8): 1743-1752.
[88] Gao H, Li J Z. Cube Algorithms for Very Large Data Warehouses[J]. Journal of Software, 2001,12(6): 830-839.
[89] Desshpande P M, Naughton J F. An Array-based Algorithm for Simultaneous Multidimensional Aggregations[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1997: 159-170.
[90] Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: Shrinking the Peta Cube[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2002: 464-475.
[91] Kong Y F, Chen H. GFSC—The storage of free cube based on graph structure[J]. Journal of Computer Research and Development, 2004, 41(10): 1652-1660.
[92] Rotem D, Srivastava J. Aggregation Algorithms for Very Large Compressed Data Warehouses[C]. Proceedings of the 25th International Conference on Very Large Data Bases. Mumbai: Morgan Kaufmann Publishers, 1999: 651-662.
[93] Poosala V. Histogram-based Estimation Techniques in Database[D]. University of Wisconsin at Madison, 1997.
[94] Aboulnaga A, Chaudhuri S. Self-Tuning Histograms: Building Histograms without Looking at Data[C]. Proceedings ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999:181-192.
[95] Gunopulos D, Kollios G, V. Tsotras J, Domeniconi C. Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2000: 463-474.
[96] Bruno N, Chaudhuri S, Gravano L. STHoles: A Multidimensional Workload-Aware Histogram[J]. ACM SIGMOD Record, 2001, 30(2): 211-222.
[97] Deshpande A, Garofalakis M, Rastogi R. Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2001: 199-210.
[98]李盛恩,王珊. Star Cube----一种高效的数据立方体实现方法[J].计算机研究与发展, 2004, 41(4): 587-593.
[99] Li S E, Wang S. Research on Closed Data Cube Technology[J]. Journal of Software, 2004, 15(8): 1165-1171.
[100]吕晓华,翁伟,贾宇波,段江娇.高效快速计算FreeCube的SPT算法[J].计算机工程与设计, 2006, 27(14): 2597-2600.
[101] Wang W, Lu H J, Feng J L, Yu J X. Condensed cube: An Effective Approach to Reducing Data Cube Size[C]. Proceedings of the 18th International Conference on Data Engineering. Los Angeles: IEEE Computer Society Press, 2002: 155-165.
[102] Sismanis Y, Deligiannakis A, Roussopoulos N, Kotidis Y. Dwarf: Shrinking the PetaCube [C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2002: 464-475.
[103] Lakeshmanan L V S, Pei J, Han J W. Quotient Cubes: How to Summarize the Semantics of a Data Cube[C]. Proceedings of the 28th International Conference on Very Large Data Bases. San Fransisco: Morgan Kaufmann Publishers, 2002: 476-487.
[104] Beyer K S, Ramakrishan R. Bottom-Up Computation of Sparse and Iceberg Cubes[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1999: 359-370.
[105] Han J W, Pei J, Dong GZ, Wang K. Efficient Computation of Iceberg Cubes with Complex Measures[C]. Proceedings of the ACM SIGMOD Conference. New York: ACM Press, 2001: 1-12.
[106]侯东风,陆昌辉,刘青宝,张维明.数据立方体计算方法研究综述[J].计算机科学, 2008, 35(10): 1-5.
[107] Shukla A, Deshpande P, Naughton J F. Materialized View Selection for Multidimensional Datasets[C]. Proceedings of the 24th International Conference on Very Large Data Bases. New York: Morgan Kaufmann Publishers, 1998: 488-499.
[108] Baralis E, Paraboschi S, Teniente E. Materialized View Selection in a Multidimensional Database[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Athens: Morgan Kaufmann Publishers, 1997:156-165.
[109] Yang J, Karlaonlen K, Li Q. Algorithm for Materialized View Design in Data Warehousing Environment[C]. Proceedings of the 23th International Conference on Very Large Data Bases. Athens: Morgan Kaufmann Publishers, 1997: 136-145.
[110] Zhang C, Yang J. Genetic Algorithm for Materialized View Selection in Data Warehouse Environments[C]. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery. Florence: Springer-Verlag, 1999: 116-125.
[111] Gupta H, Mumick I S. Selection of Views to Materialize in a Data Warehouse[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(1): 24-43.
[112]冷春霞,数据仓库中物化视图的选择方法[J].华东理工大学学报:自然科学版, 2005, 31(2): 203-207.
[113]顾军华,赵秀丽,谭庆.蚁群算法在物化视图选择问题中的应用[J].计算机应用, 2007,27(11): 2763-2765.
[114]王伟皓,郑宁.物化视图选择中权限因子的设计[J].计算机应用与软件, 2007, 24(10): 104-106.
[115]王自强,孙霞,张德贤.数据仓库中用于视图选择的增强遗传算法[J].小型微型计算机系统, 2007, 28(2): 267-371.
[116] Yan Y, Wang P, Wang C, et al. Anne: An Efficient Framework on View Selection Problem[C]. Proceedings of the 6th Asia-Pacific Web Conference on Advanced Web Technologies and Applications. Hangzhou: Springer-Verlag, 2004: 384-394.
[117] Deshpande P, Ramaswamy K, Shukla A, Naughton J. Caching Multidimensional Queries Using Chunks[C]. Proceedings of ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998: 259-270.
[118] Choi C H, Yu J X, Lu H J. Dynamic Materialized View Management based on Predicates[C]. Proceedings of the 5th Asia-Pracific Web Conference on Web Technologies and Applications. Xi’an: Springer-Verlag, 2003: 583-594.
[119]张柏礼,孙志挥,周晓云,张净. DMVR:一种基于Cache的动态物化视图置换算法[J].应用科学学报, 2006, 24(2): 159-164.
[120] Aouiche K, Jouve P E, Darmont J. Clustering-Based Materialized View Selection in Data Warehouses[C]. Proceedings of the 10th East European Conference on Advances in Databases and Information Systems. Saloniki: Springer-Verlag, 2006: 81-95.
[121]薛永生,林子雨,段江娇,吕晓华,张伟.用多用户多窗口方法处理多维视图动态选择[J].计算机研究与发展, 2004, 41(10): 1703-1711.
[122]张东站,黄宗毅,薛永生. NDSMMV—种多维数据集物化视图动态选择新策略[J].计算机研究与发展, 2008, 45(5): 901-908.
[123]张柏礼,孙志挥,周晓云,杨宜东,朱玉全.静态物化视图的动态Cache优化算法[J].软件学报, 2006, 17(5): 1213-1221.
[124]袁贞明.基于样例的空间数据检索技术研究[D].杭州:浙江大学, 2005.
[125] Worboys M. GIS: A computing perspective[M]. London: Taylor and Francis, 1995.
[126]马智民,俞全宏,姜作勤.应用地理信息系统设计与实现[M].西安:西安地图出版社, 1996.
[127] Gaede V, Gunther O. Multimensional Access Methods[J]. ACM Computing Surveys, 1998, 30(2): 170-231.
[128] Liang Y, Zhang H. An Algorithm for Multi-Way Distance Join Query[C]. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Taipei: IEEE Press, 2006: 412-417.
[129]梁银,张虹.一种多路空间距离连接查询处理方法[J].计算机应用, 2008, 28(1): 155-158.
[130] Gray J, Chaudhuri S, Bosworth A, et al. Data Cube: a Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub-Totals[J]. Data Mining and Knowledge Discovery, 1997: 29-53.
[131] Shekhar S, Chawla S. Spatial Databases: A Tour[M]. Prentice Hall: New Jersey, 2003.
[132] Liang Y, Zhang H. Design and Implementation of Enterprise Spatial Data Warehouse[C]. Proceedings of IFIP Conference. Beijing: Springer-Verlag, 2007: 75-83.
[133]梁银,张虹.企业空间数据仓库的构建方法[J].山东大学学报:理学版. 2007, 42(9): 51-55.
[134] Zhang L, Li Y, Rao F, et al. An Approach to Enabling Spatial OLAP by Aggregating on Spatial Hierarchy[C]. Proceedings of DaWak Conference. Heidelberg: Springer-Verlag, 2003: 35-44.
[135] Rao F, Zhang L, Yu X L, et al. Spatial Hierarchy and OLAP-favored Search in Spatial Data Warehouse[C]. Proceedings of the 6th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2003: 48-55.
[136] Gupta H, Mumick I S. Selection of Views to Materialize under a Maintenance Cost Constraint[C]. Proceedings of 8th International Conference of Data Theory. Heidelberg: Springer-Verlag, 1999: 453-470.
[137] Jeffrey X Y, Yao X, et al. Materialized View Selection as Constrained Evolutionary Optimization[J]. IEEE Transactions on Systems, Man and Cybernetics, 2003, 33(4): 458-467.
[138] Zhang C, Yao X, Yang J. An Evolutionary Approach to Materialized Views Selection in a Data Warehouse Environment[J]. IEEE Transactions on Systems, Man and Cybernetics, 2001, 31(3): 282-294.
[139]梁银,张虹.空间区域聚集查询方法研究[J].计算机工程与应用, 2009, 45(25): 135-137.
[140] Harinarayan V, Rajaraman A, Ullman J D. Implementing Data Cubes Efficiently[C]. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1996: 205-216.
[141] Nadeau T P, Teorey T J. Achieving Scalability in OLAP Materialized View Selection[C]. Proceedings of the 5th ACM International Workshop on Data Warehousing and OLAP. New York: ACM Press, 2002: 28-34.
[142]童云海,谢昆青,唐世渭.空间Cube计算方法[J].计算机工程与应用, 2002(6): 1-5.
[143]童云海,谢昆青,唐世渭.空间数据仓库模型和空间Cube计算方法[J].计算机科学, 2002, 29(10): 1-5.
[144] Han J , Kamber M. Data Mining: Concepts and Techniques[M] . Morgan Kaufmann Publishers , 2000.
[145] Wilkin G A, Huang X Z. K-Means Clustering Algorithms: Implementation and Comparison[C]. Proceedings of the 2th International Multi-Symposium of Computer and Computational Sciences. Iowa: IEEE Press, 2007: 133-136.
[146] Ordonez C, Omiecinski E. FREM: Fast and robust EM clustering for large data sets[C]. Proceedings of ACM CIKM International Conference on Information and Knowledge Management. McLean: ACM Press, 2002: 590-599.
[147] Kaufman L, Rousseeuw P J. Finding Groups in Data: an Introduction to Cluster Analysis[M]. John Wiley & Sons, 1990.
[148] Ng R T, Han J W. Effencient and Effective Clustering Methods for Spatial Data Mining[C]. Proceedings of the 20th International Conference on Very Large Data Bases. Santiago: VLDB Press, 1994: 144-155.
[149] Zhang T, Ramakrishnan R, Livny M. BIRCH: An Efficient Data Clustering Method for Very Large Databases[C]. Proceedings of ACM SIGMOD International Conference on Management of Data. Montreal: ACM Press, 1996: 103-114.
[150] Karypis G, Han E H, Kumar V. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling[J]. IEEE Computer, 1999, 32(8): 68-75.
[151] Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithm for Large Databases[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998: 73-84.
[152] Guha S, Rastogi R, Shim K. ROCK: A Robust Clustering Algorithm for Categorical Attributes[C]. Proceedings of 15th. International Conference on Data Engineering. Australia: ICDE press, 1999: 512-521.
[153] Hinneburg A, Keim D A. An Efficient Approach to Clustering in Large Multimedia Databases with Noise[C]. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining. New York: AAAI Press, 1998: 58-65.
[154] Ankerst M, Breuning M M, Kriegel H P, Sander J. OPTICS: Ordering Points to Identify the Clustering Structure[C]. Proceedings of ACM SIGMOD Internetional Conference on Management of Data. Philadelphia: ACM Press, 1999: 49-60.
[155] Ester M, Kriegel H P, Sander J, Xu X W. A Density - based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]. Proceedings of the 2th International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231.
[156] Wang W, Yang J, Muntz R. STING: A Statistical Information Grid Approach to Spatial Data Mining[C]. Proceedings of the 23th VLDB Conference. Athens, 1997: 186-195.
[157] Sheikholeslami G, Chatterjee S, Zhang A D. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases[C]. Proceedings of the 24th VLDB Conference. New York, 1998: 428-439.
[158] Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic Subspace Cclustering of High Dimensional Data for Data Mining Applications[J]. ACM SIGMOD Record, 1998, 27(2): 94-105.
[159]杨风召.高维数据挖掘技术研究[M].南京:东南大学出版社, 2007.
[160]林子雨,杨冬青,宋国杰.实时主动数据仓库中多维数据实视图的选择[J].软件学报, 2008, 19(2): 301-313.
[161] Theodoridis Y, Stefanakis E, Sellis T. Efficient Cost Model for Spatial Queries Using R-tree[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(1): 1-21.
[162] Kalnis P, Mamoulis N, Papadias D. View Selection Using Randomized Search[J]. Data & Knowledge Engineering, 2002, 42(1): 89-111.
[163]谭红星,周龙骧.多维数据实视图的动态选择[J].软件学报, 2002, 13(6): 1090– 1096.
[164]梁银,张虹.一种空间数据仓库动态物化视图选择算法[J].计算机工程与应用, 2008, 44(28): 122-124.