松花江流域水资源空间数据仓库的设计与实现被引量：1

The design and implementation of spatial data warehouse for Songhua river basin water resources management

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摘要针对水资源数据具有复杂、数据量大、多源异构等特点,论文提出了松花江流域水资源空间数据仓库的总体设计方案,在数据集成、数据清洗、数据简化与数据转换等预处理操作的基础上,构建了多维数据模型,并实现了基于空间OLAP的多维数据分析,为水资源决策分析提供有效的数据支持。 In allusion to complexity, abundance, multi-sources and heterogeneity of water resources data, spatial data warehouse technology is adopted to design subject-oriented, integrated and steady multidimensional datasets, in order to supply efficient data for water resources decision. This paper designs the spatial data warehouses scheme of water resources in Songhua River basin, and reali- zes data pretreatment including data integration, data cleaning, data transformation and data reduction. Then it constructs star schema data model and analyzes multidimensional data based on spatial OLAP. Finally it archives the improvement of knowledge discovery and data mining.

作者廖晓玉佟志军杨令宾付炜

机构地区燕山大学信息科学与工程学院电子与通信工程系东北师范大学城市与环境科学学院

出处《测绘科学》 CSCD 北大核心 2009年第2期219-221,183,共4页 Science of Surveying and Mapping

基金国家“十五”科技攻关项目(2004BA528B-3-1)

关键词空间数据挖掘松花江多维数据模型 SOLAP spatial data digging Songhua River multidimensional data model SOLAP

分类号 P208 [天文地球—地图制图学与地理信息工程]

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参考文献10

1夏勇,王丽珍,胥玲芳,黄永平.澜沧江(湄公河)流域水资源空间数据仓库设计及实现[J].计算机工程与应用,2004,40(19):175-178. 被引量：4
2Irtishad Ahmad, Salman Azhar. Development of a decision support system using data warehousing to assist builders developers in site selection [ J ]. Automation in Construction,2004, (13) : 525-542.
3王晓明,高勇,刘玉玲,邬伦.面向水环境管理的空间数据仓库构建[J].计算机应用研究,2005,22(11):195-197. 被引量：5
4石磊,石云.空间数据仓库中维和度量的建模[J].计算机应用,2000,20(10):19-20. 被引量：5
5Nebojsa Stefanovic, Jiawei Han, Krzysztof Koperski. Object-Based Selective Materialization for Efficient Impiementation of Spatial Data Cubes [ J ]. Transactions on Knowledge and Data Engineering, 2000, 12 (6) .
6李一军,樊博.空间OLAP技术研究[J].管理科学学报,2003,6(4):9-16. 被引量：2
7Anindya Datta, Helen Thomas. The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses [ J ]. Decision Support Systems, 1999, 27: 289-301.
8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. 被引量：15
9Jiawei Han, Nebojsa Stefanovic, Krzysztof Koperski. Selective Materialization: An Efficient Method for Spatial Data Cube Construction [ J ]. Santa Barbara, CA: Advances in Digital Libraries Conf. (ADL98), 1998, (4) .
10Nebojsa Stefanovic, Jiawei Han. Object-based selective materialization for efficient implementation of spatial data cubes [ J ]. IEEE Transactions on Knowledge Data Engineering. 2000, 12(6) : 938-958.

二级参考文献33

1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. 被引量：16
2Han Jiawei Kamber.Data Mining:Concepts and Technique[M].北京:机械工业出版社,2001..
3Koperski Krzystaf. Mining knowledge in geographical data[J]. Communications of ACM, 1998, 4(2) : 1--3.
4Han Jiawei, Stefanovic Nebojsa. Geominer: A system prototype for spatial data mining description of geominer system[J]. Communications of ACM, 1997, 4(9) : 553--554.
5Haier Baazaoui, Zghal Samifciiz, Henda Ben Ghezala. Exploration techniques of the spatial data warehouses : Overview and application to incendiary domain[J]. IEEE Conference on Computer System, 2001, 7(3): 21--23.
6Venky Harinarayan, Anand Rajaraman. Implementing data cubes efficiently [J ] . Communications of ACM SIGMOD International Conference, 1996, 5(1) : 205--216.
7Zhang Chuan, Yao Xin, Yang Jian. An evolutionary approach to materialized views selection in a data warehouse environment[Jl. IEEE Transaction on System, Man,and Cybernetics, 2001, 8(31): 3--8.
8L in Wen-yang, Kuo I-chung. OLAP data cubes configuration with genetic algorithms[J]. IEEE Transactionson Knowledge and Data Engineering, 2000, 2(5): 132--143.
9Han Jiawei, Stefanovic Nebojsa. Selective materialization: A efficient method for spatial data cube construction [J]. Communications of ACM, 1998, 4(1): 7--12.
10Stefanovic Nibojsa, Hart Jiawei, Kooerski Krzysztof. Object-based selective materialization for efficient implementation of spatial data cubes[J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(6) : 2--7.