基于GIS的长江口及邻近海域环境时空多维分析
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摘要
陆海相互作用是“全球变化”背景下的国际研究热点,而大型河口及其邻近海域是实施这一研究最为重要的场所。河口--近海系统作为地球四大圈层(水圈、生物圈、岩石圈、气圈)的交汇地带,其陆海相互作用最为活跃、生态环境脆弱、演变机制复杂、科学内涵丰富。河流入海物质通过复杂的河口界面过程来影响河口—近海环境的变化,而沉积动力过程和生物地球化学过程及其耦合作用是探明河口界面过程的钥匙。
因河口环境具有动态变化特点,而且其环境总是处于不断变化的时空之中,因此研究河口及其邻近海域环境动态也必须同时考虑时间和空间这两个方面,对环境动态进行时空一体化研究是河口及其邻近海域环境研究的必然趋势。对河口及其邻近海域环境进行时空分析,可以在时间和空间上把握环境要素的分布与变化情况,发现环境要素的变化规律,同时也可为政府提供重要决策信息。长江河口及其近海系统具有鲜明的区域特色,突出表现在:淤泥质河口海岸、高浑浊度水体和宽浅的陆架。为此,选择长江河口及近岸海域,深入系统地研究入海物质通量变异背景下河口及邻近海域的沉积动力过程、生物地球化学过程及其海岸带环境效应,不仅将为我国海岸带和流域开发之重大工程决策、海岸带资源利用和环境保护提供科学依据,而且将极大地丰富和发展全球陆海相互作用研究的理论体系。
多维数据库(Multi Dimensional Database,MDD)是在数据仓库的应用中发展起来的,多维数据库可以简单地理解为:将数据存放在一个n维数组中,而不是像关系数据库那样以记录的形式存放。多维数据模型能使数据建模更加简单,因为开发人员能够方便地用它来描述出复杂的现实世界结构,而不必忽略现实世界的问题,或把问题强行表现成技术上能够处理的形态,而且多维数据模型使执行复杂处理的时间大大缩短。与关系数据库相比,多维数据库的优势在于可以提高数据处理速度,加快反应时间,提高查询效率。
河口及其邻近海域的环境数据具有多维性,即时间、空间(经度、纬度、深度)等维度,各种环境测量数据则为各个维度交点的度量值,因此适合用多维数据结构进行建模及存储。同时多维数据结构可以方便地进行各种地学分析,如按平面、断面进行切片可以得到地学研究中经常使用的平面图、断面图等。
GIS是计算机科学、地理学、测量学、地图学等多门学科综合的技术。GIS作为处理地理数据的输入、输出、管理、查询、分析和辅助决策工具,以其出色的数据集成和空间数据处理及可视化表现能力,为我们管理和分析各种资料提供了一种有效的手段。
本文将GIS技术和多维数据库技术引入到长江口及邻近海域环境研究中,提出了长江口及邻近海域海洋环境的时空多维数据模型,研究了海洋环境数据进行时空分析的方法;根据时空多维分析方法及模式,对长江口及邻近海域部分主题的环境数据进行了时空分析,研究分析了悬浮体、营养盐、长江入河口水沙等主题的时空分布规律;探讨了时空多维数据的共享与发布技术,并基于WebGIS技术实现了长江口环境时空多维数据的共享与发布。
The land-ocean interaction is a hot research under the background of“the global change”. Moreover large-scale estuary and surrounding area is the most important region to implement this research. Estuaries and coastal system is the connection region of Earth Geosphere (hydrosphere, biosphere, lithosphere, atmosphere), and its land-ocean interaction is the most active, ecological environment is frail and mechanism is complex. Sediment and water discharge could affect the environment in estuary and coastal system via the complex process of estuary interface which could be explained by sediment dynamic process and biogeochemistry process.
The estuary environment is dynamic and complex; therefore it is important to consider two aspects - time and space which are an inevitable trend for interation in the environmental research of estuary and surrounding area. Utilizing the space and time analysis in the environment of estuary and coastal area, can grasp the distribution and changes of environment essential factors in the time and space, discover the changing rule of environment essential factors, and provide important decision information for the government in the same time.
Changjiang (Yangtze) estuary and surrounding area has bright region characteristic and displays prominently in: muddy coast, high turbidity water and shallow shelf. Therefore, it is important to choose Changjiang (Yangtze) estuary and coastal area to investigate the sediment dynamic process, biogeochemistry process and coastal environment in the background of sediment flux variation. It could not only provide scientific basis in the decision of important project, coastal zone resources using and environmental protection, but also develop the theory of land ocean interaction.
Multi Dimensional Database (MDD) developed in the application of date warehouse. It could be explained in a simple way: Stores data in a multi-dimensional array, but didn’t only record them as a relational database. The multi-dimensional data model can make the data modeling to be simpler. Because the research person can describe the structure of the complex real world conveniently, didn’t need to neglect the question in real world, or display the shape which could be improved in the technology, moreover the multi-dimensional data model could shorter the time in implementing complex process. Compares with the relational database, the advantage of multi-dimensional database is in raising the speed of data processing, speeding up the reaction time and raising the inquiry efficiency.
The environmental data in estuary and surrounding area is multi-dimensional array, namely time, space (longitude, latitude and depth) and et al. Each kind of environment investigated data is a measure value for every dimension intersectional point. Therefore it fits for the data of multi-dimensional construction in modeling and storage. At the same time, the date of multi-dimensional construction could implement conveniently in many kinds of geology analyses, such as analyzing in geometric horizontal section and cross section, which are used frequently in geology analysis.
GIS is comprehensive technology in computer, geography, metrology, cartology and so on. It as an instrument in processing the data of geology in input, output, management, inquiry, analysis and aiding in decision making, provides an efficient method in the information of management and analysis by its ability of data integration, processing and visualization performance.
This article introduces the GIS and the multi-dimensional database into the environment research in Changjiang (Yangtze) estuary and surrounding area, proposed multi-dimensional data model of environment in Changjiang (Yangtze) estuary and surrounding area, studies the method of marine environmental data in implementing space and time analysis. Also analyzes parts of multi-dimensional environmental data in Changjiang (Yangtze) estuary and surrounding area via the method of space and time analysis, interprets the distribution and variation of suspended matter, nutrient, sediment discharge and water discharge, issues and shares the multi-dimensional environmental data based on the WebGIS technology.
因河口环境具有动态变化特点,而且其环境总是处于不断变化的时空之中,因此研究河口及其邻近海域环境动态也必须同时考虑时间和空间这两个方面,对环境动态进行时空一体化研究是河口及其邻近海域环境研究的必然趋势。对河口及其邻近海域环境进行时空分析,可以在时间和空间上把握环境要素的分布与变化情况,发现环境要素的变化规律,同时也可为政府提供重要决策信息。长江河口及其近海系统具有鲜明的区域特色,突出表现在:淤泥质河口海岸、高浑浊度水体和宽浅的陆架。为此,选择长江河口及近岸海域,深入系统地研究入海物质通量变异背景下河口及邻近海域的沉积动力过程、生物地球化学过程及其海岸带环境效应,不仅将为我国海岸带和流域开发之重大工程决策、海岸带资源利用和环境保护提供科学依据,而且将极大地丰富和发展全球陆海相互作用研究的理论体系。
多维数据库(Multi Dimensional Database,MDD)是在数据仓库的应用中发展起来的,多维数据库可以简单地理解为:将数据存放在一个n维数组中,而不是像关系数据库那样以记录的形式存放。多维数据模型能使数据建模更加简单,因为开发人员能够方便地用它来描述出复杂的现实世界结构,而不必忽略现实世界的问题,或把问题强行表现成技术上能够处理的形态,而且多维数据模型使执行复杂处理的时间大大缩短。与关系数据库相比,多维数据库的优势在于可以提高数据处理速度,加快反应时间,提高查询效率。
河口及其邻近海域的环境数据具有多维性,即时间、空间(经度、纬度、深度)等维度,各种环境测量数据则为各个维度交点的度量值,因此适合用多维数据结构进行建模及存储。同时多维数据结构可以方便地进行各种地学分析,如按平面、断面进行切片可以得到地学研究中经常使用的平面图、断面图等。
GIS是计算机科学、地理学、测量学、地图学等多门学科综合的技术。GIS作为处理地理数据的输入、输出、管理、查询、分析和辅助决策工具,以其出色的数据集成和空间数据处理及可视化表现能力,为我们管理和分析各种资料提供了一种有效的手段。
本文将GIS技术和多维数据库技术引入到长江口及邻近海域环境研究中,提出了长江口及邻近海域海洋环境的时空多维数据模型,研究了海洋环境数据进行时空分析的方法;根据时空多维分析方法及模式,对长江口及邻近海域部分主题的环境数据进行了时空分析,研究分析了悬浮体、营养盐、长江入河口水沙等主题的时空分布规律;探讨了时空多维数据的共享与发布技术,并基于WebGIS技术实现了长江口环境时空多维数据的共享与发布。
The land-ocean interaction is a hot research under the background of“the global change”. Moreover large-scale estuary and surrounding area is the most important region to implement this research. Estuaries and coastal system is the connection region of Earth Geosphere (hydrosphere, biosphere, lithosphere, atmosphere), and its land-ocean interaction is the most active, ecological environment is frail and mechanism is complex. Sediment and water discharge could affect the environment in estuary and coastal system via the complex process of estuary interface which could be explained by sediment dynamic process and biogeochemistry process.
The estuary environment is dynamic and complex; therefore it is important to consider two aspects - time and space which are an inevitable trend for interation in the environmental research of estuary and surrounding area. Utilizing the space and time analysis in the environment of estuary and coastal area, can grasp the distribution and changes of environment essential factors in the time and space, discover the changing rule of environment essential factors, and provide important decision information for the government in the same time.
Changjiang (Yangtze) estuary and surrounding area has bright region characteristic and displays prominently in: muddy coast, high turbidity water and shallow shelf. Therefore, it is important to choose Changjiang (Yangtze) estuary and coastal area to investigate the sediment dynamic process, biogeochemistry process and coastal environment in the background of sediment flux variation. It could not only provide scientific basis in the decision of important project, coastal zone resources using and environmental protection, but also develop the theory of land ocean interaction.
Multi Dimensional Database (MDD) developed in the application of date warehouse. It could be explained in a simple way: Stores data in a multi-dimensional array, but didn’t only record them as a relational database. The multi-dimensional data model can make the data modeling to be simpler. Because the research person can describe the structure of the complex real world conveniently, didn’t need to neglect the question in real world, or display the shape which could be improved in the technology, moreover the multi-dimensional data model could shorter the time in implementing complex process. Compares with the relational database, the advantage of multi-dimensional database is in raising the speed of data processing, speeding up the reaction time and raising the inquiry efficiency.
The environmental data in estuary and surrounding area is multi-dimensional array, namely time, space (longitude, latitude and depth) and et al. Each kind of environment investigated data is a measure value for every dimension intersectional point. Therefore it fits for the data of multi-dimensional construction in modeling and storage. At the same time, the date of multi-dimensional construction could implement conveniently in many kinds of geology analyses, such as analyzing in geometric horizontal section and cross section, which are used frequently in geology analysis.
GIS is comprehensive technology in computer, geography, metrology, cartology and so on. It as an instrument in processing the data of geology in input, output, management, inquiry, analysis and aiding in decision making, provides an efficient method in the information of management and analysis by its ability of data integration, processing and visualization performance.
This article introduces the GIS and the multi-dimensional database into the environment research in Changjiang (Yangtze) estuary and surrounding area, proposed multi-dimensional data model of environment in Changjiang (Yangtze) estuary and surrounding area, studies the method of marine environmental data in implementing space and time analysis. Also analyzes parts of multi-dimensional environmental data in Changjiang (Yangtze) estuary and surrounding area via the method of space and time analysis, interprets the distribution and variation of suspended matter, nutrient, sediment discharge and water discharge, issues and shares the multi-dimensional environmental data based on the WebGIS technology.
引文
[1]陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社,1999.
[2]龚健雅.当代GIS的若干理论与技术[M].武汉:武汉测绘科技大学出版社,1999.
[3]边馥苓.地理信息系统工程.北京:测绘出版社.1996.
[4]承继成.数字地球与国家空间信息基础设施[M].北京:清华大学出版社,1999.
[5]陈常松.面向数据共享的GIS语义表达理论的初步研究.测绘学报,2000.1.
[6]陈常松,何建邦.面向GIS数据共享的概念模型设计研究,遥感学报,1999,3(3).
[7]龚健雅.当代地理信息系统的发展趋势.厦门:中国GIS协会第二次团体会员代表大会及海峡两岸GIS发展研讨会论文集[C] ,2002.
[8]黄裕霞,陈常松,何建邦. GIS互操作及其体系结构.地理研究2000. 3.
[9]孔云峰,林珲.基于万维网的地理信息系统集成研究.遥感学报,1998,2(2).
[10]李德仁.信息高速公路、空间数据基础设施与数字地球[J].测绘学报,1999(1).
[11]李德仁.龚健雅,边馥苓.地理信息系统导论[M].北京:测绘出版社,1993.
[12]李德仁,李清泉.地球空间信息学与数字地球[N].电子科技导报,1999.
[13]李德仁,李清泉.论地球空间信息技术与通信技术的集成[J].武汉大学学报(信息科学版),2001,26(1):1-7
[14]“IHO数字测深数据中心工作项目”, www.hydro.gov.cn/xsyd/show.asp.
[15]赵丽宁,李一凡,赵德鹏.数字海洋空间数据库的构建[J].大连海事大学学报, 2002,28(1):34-37.
[16]恽才兴.海洋地理信息系统(MGIS)研究进展[J].海洋地质动态,2002,18(1):23-26.
[17]蔡中祥.基于GIS的长江河口空间决策支持研究.华东师范大学博士论文,2005.
[18]刘展等.适于“数字海底”的GIS数据仓模型初探[J].西安石油大学学报,2004,19(1):77-81.
[19]甄洪排等.海洋测绘资料综合数据仓库的设计[J].海洋测绘,2006,26(1):63-65.
[20]侯文峰.中国“数字海洋”发展的基本构想[J].海洋通报,1999,18(6):1-10.
[21]翟世奎,张怀静,范德江,等.长江口及其邻近海域悬浮体浓度与浊度的对应关系[J ] .环境科学学报,2005 ,25 (5) :6932699. [ZHAI Shi-kui, ZHANG Hua-jing, FAN De-jiang, et al. Corresponding relationships between suspended mat ter concent ration and turbidity on Changjiang Estuary and adjacent sea area [J]. Acta Scientiae Circumstantiae, 2005, 25 (5): 693-699. ]
[22]张怀静,翟世奎,范德江,郭志刚,魏立青,种法运.三峡工程一期蓄水后长江口悬浮体形态及物质组成[J].海洋地质与第四纪地质,2007,27(2):1-10.
[23]张怀静,翟世奎,范德江,郭志刚,于增慧,曹立华,杨荣民,张晓东.三峡工程一期蓄水后长江口及其邻近海域悬浮物浓度分布特征[J].环境科学,2007,28(8):1655-1661.
[24]卢海建,翟世奎,许淑梅.三峡工程一期蓄水后长江口及其邻近海域沉积地球化学研究[J].海洋地质与第四纪地质,2006,26(5):11-19.
[25]姚家奕.多维数据分析原理与应用.清华大学出版社,2004. 5
[26]周根贵.数据仓库与数据挖掘.浙江大学出版社,2004.
[27]【美】Ralph Kimball,【美】Margy Ross.数据仓库工具箱:维度建模的完全指南.电子工业出版社,2003
[28]邵晓巍,赵长安,赵景珠,邵长胜.数据仓库的多维数据模型研究[J].计算机工程与应用,2004(11):4-8.
[29] W.H.Inmon. Building the Data Warehouse. 2nd edition. John Wiley and Sons,2000. pp.20-44.
[30] Daniel R. Dolk.Integrated model management in the data warehouse era. European Journal of Operational Research. 2000. Vo1.122, No.2.pp. 199-218
[31] Bala Iyer,G. Shankaranarayanan,Melanie L. Lenard. Model management decision environment:a Web service prototype for spreadsheet models. Decision Support Systems. 2005, Vo1.40, No.2.pp.283-304
[32] Blanning R.H. A Relational Framework for Join Implementation in Model
[33] Management Systems. Decision Support Systems. 1985.Vo1.1, No.l.pp.69-81
[34] Basu A. R.W.Blanning. Model Integration Using Metagraphs. Information SystemResearch. 1994.Vo1.5,No.3.pp.195-218
[35] Basu A. R.W.Blanning. The Analysis of Assumptions in Model Bases Using Metagraphs. Management Science. 1998.Vo1.44,No.7.pp.982-995
[36] Roger Alan Pick, Gary Klein. Model management as a component of a knowledge management system:Capturing modeling knowledge in the enterprise. Preceeding of the Eighth Americas Conference on Information Systems.Dallas,USA.2002
[37] Bhargava H.K.,R. Krishnan, R.Muller. Decision Support on Demand: Emerging Electronic Markets for Dedision Technologies. Decision Support Systems. 1997. Vo1.19, No.3.pp. 193-214
[38] Kaushal Chari. Model Composition Using Filter Spaces. Information Systems Research. 2002. Vo1.13, No.1.pp.15-35
[39] Keun-Woo Lee, Soon-Young Huh. A model-solver integration framework for autonomous and intelligent model solution. Decision Support Systems. 2006, Vol.42, No.2. pp.926-944
[40] Kingsley Gnanendran, R.P. Sundarraj. Alternative model representations and computing capacity: Implications for model management, Decision Support Systems, 2006, Vo1.42, No.3. pp.1413-430
[41] E.F.Codd,S.B.Codd, C.T.Salley. Providing OLAP to User-Analysis:an IT Mandate. White Paper. Arbor Software Corporation, 1993.
[42] Core. A. the Digital Earth:understanding our place in the 21st century [J]. The Australian Surveyor,1998,43(2):89-91.
[43] Han J,Kamber M. Data Mining Concept and Techniques [M]. Morgan Kaufmann Publishers. 2001.
[44] Wooley B,Bridges S,Hodges Jetal. Scaling the Data Mining Step in Knowledge Discovery Using Oceanographic Data [M]. IEA/AIE 2000:85-92.
[45] Guo D,Evangelinos C,Patrikalakis N. Flow Feature Extraction in Oceangraphic Visualization [M]. Computer Graphics International 2004: 162-173.
[46] Jeusfeld M.A., T.X. Bui. Distributed Decision Support and Organizational Connectivity. Decision Support Systems. 1997. Vo1.19, No.3. pp.215-225
[47] Liang T. Development of a Knowledge-based Model Mnagement system. Operational Research. 1988. Vo1.36, No.6. pp.849-863
[48] Dutta A. A.Basu. An Artificial Intelligence Approach to Model Management in Decision Support Systems. IEEE Computer. 1984. Vo1.17,No.9. pp.89-97
[49] Shaw M.J., P. Tu, P. De. Applying Machine Learning to Model Mangement in Decision Support Systems. Decision Support Systems. 1988. No.4. pp.285-305
[50] Holocher M.R. Michalski, D.Solte, F. Vicna. MIDA: An open Systems Architecture for Model-Oriented Integration of Data and Algorithms. Decision Support Systems. 1997. Vo1.20, No.2. pp.135-147
[51] Ba S., K.R. Lang, A.B. Whinston. Enterprise Decision Support Using Intranet Technology. Decsion Support Systems.1997.Vo1.20, No.2. pp.99-134
[52] Agrawal R, Gupta A, Sarawagi S. Modeling multidimensional databases. Proceedings of the 13th International Conference on Data Engineering. Los Alamitos, CA: IEEE Society Press. 1997.pp.105-I 16
[53] Muhanna W.A. On the Organization of Large Shared Model Bases. Annal Operational Research. 1992. No.38. pp.359-396
[54] Ding Q,Perrizo W. Association Rule Mining on Remotely Sensed Images Using P-tree [M]. PAKDD 2002:66-79.
[55] Kitamoto A. Data Mining for Typhoon Image Collection [M]. MDM/KDD 2001:68-77.
[56] Kitamoto A. Evolution Map:Modeling State Transition of Typhoon Image Sequences by Spatio-Temporal Clustering [M]. Discovery Science,2002: 283-290.
[57] Kitamoto A. Typhoon Analysis and Data Mining with Kernel Methods [M]. SVM 2002:237-248.
[58] Rafanelli M, Shoshani A. STORM: a Statistical Object Representation Model. Data Engineering Bulletion. 1990. Vo1.13, No.3. pp.12-18
[59] Gyssens M, Lakshmanan L V S. A Foundation for Multi-dimensional Databases. Proceedings of the 23rd Conference on very large data bases. San Francisco, CA: Morgan Kaufmann Publishers, Inc. 1997. pp.106-115
[60] Li C, Wang X S. A Data Model for Supporting On-line Analytical Processing. Proceedings of the 5th International Conference on Information and Knowledge Management. New York: Springer-Verlag, 1996. pp.81-88
[61] Lehner W. Modeling Large Scale OLAP Scenarios. Proceedings of the 6th International Conference on Extending Database Technology. New York: Springer-Verlag.1998. pp.153-167
[62] Datta A, Thomas H, A Conceptual Model and Algebra for On-line Analytical Processing in Decision Support Databases. Proceedings of the 7th Annual Workshop on Information Technologies and Systems. San Mateo, CA: Morgan Kaufman Publishers, Inc.1997. pp.91-100
[63] Pedersen T B, Jensen C S, Multidimensional Data Modeling for complex data. Proceedings of the 15th International Conference on Data Engineering. Los Alamitos, CA: IEEE Society Press. 1999. pp.336-345
[64] Gray J, Bosworth A, Layman A et al. Data Cube: A Relational Aggregation Operator Generalizing Group-bv. Cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1997. Vol.l No. l.pp.29-54
[65] R.Kimbal, L.Reeves, M.Ross, W.T.Waite, The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses. John Wiley&Sons, 1998.
[66] V. Harinarayan, A. Rajaraman, J. Ullman. Implementing Data Cubes Efficiently. In: Proc. of the 1996 ACM SIGMOD Int'1 Conf. on Management of Data, ACM Press, 1996.pp.205-216.
[67] W.S.Yang, Y.D.Chung, M.H.Kim.The RD-Tree: a structure for processing Partial-MAX/ MIN queries in OLAP. Information Sciences, 2002. Vol. 146, No.4. pp.137-149.
[68] C.T. Ho, R. Agrawal, N. Megiddo, R. Srikant. Range Queries in OLAP Data Cubes. In: Proc. of the ACM SIGMOD Conf. on the Management of Data, 1997, pp.73-88.
[69] H. Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1989.
[2]龚健雅.当代GIS的若干理论与技术[M].武汉:武汉测绘科技大学出版社,1999.
[3]边馥苓.地理信息系统工程.北京:测绘出版社.1996.
[4]承继成.数字地球与国家空间信息基础设施[M].北京:清华大学出版社,1999.
[5]陈常松.面向数据共享的GIS语义表达理论的初步研究.测绘学报,2000.1.
[6]陈常松,何建邦.面向GIS数据共享的概念模型设计研究,遥感学报,1999,3(3).
[7]龚健雅.当代地理信息系统的发展趋势.厦门:中国GIS协会第二次团体会员代表大会及海峡两岸GIS发展研讨会论文集[C] ,2002.
[8]黄裕霞,陈常松,何建邦. GIS互操作及其体系结构.地理研究2000. 3.
[9]孔云峰,林珲.基于万维网的地理信息系统集成研究.遥感学报,1998,2(2).
[10]李德仁.信息高速公路、空间数据基础设施与数字地球[J].测绘学报,1999(1).
[11]李德仁.龚健雅,边馥苓.地理信息系统导论[M].北京:测绘出版社,1993.
[12]李德仁,李清泉.地球空间信息学与数字地球[N].电子科技导报,1999.
[13]李德仁,李清泉.论地球空间信息技术与通信技术的集成[J].武汉大学学报(信息科学版),2001,26(1):1-7
[14]“IHO数字测深数据中心工作项目”, www.hydro.gov.cn/xsyd/show.asp.
[15]赵丽宁,李一凡,赵德鹏.数字海洋空间数据库的构建[J].大连海事大学学报, 2002,28(1):34-37.
[16]恽才兴.海洋地理信息系统(MGIS)研究进展[J].海洋地质动态,2002,18(1):23-26.
[17]蔡中祥.基于GIS的长江河口空间决策支持研究.华东师范大学博士论文,2005.
[18]刘展等.适于“数字海底”的GIS数据仓模型初探[J].西安石油大学学报,2004,19(1):77-81.
[19]甄洪排等.海洋测绘资料综合数据仓库的设计[J].海洋测绘,2006,26(1):63-65.
[20]侯文峰.中国“数字海洋”发展的基本构想[J].海洋通报,1999,18(6):1-10.
[21]翟世奎,张怀静,范德江,等.长江口及其邻近海域悬浮体浓度与浊度的对应关系[J ] .环境科学学报,2005 ,25 (5) :6932699. [ZHAI Shi-kui, ZHANG Hua-jing, FAN De-jiang, et al. Corresponding relationships between suspended mat ter concent ration and turbidity on Changjiang Estuary and adjacent sea area [J]. Acta Scientiae Circumstantiae, 2005, 25 (5): 693-699. ]
[22]张怀静,翟世奎,范德江,郭志刚,魏立青,种法运.三峡工程一期蓄水后长江口悬浮体形态及物质组成[J].海洋地质与第四纪地质,2007,27(2):1-10.
[23]张怀静,翟世奎,范德江,郭志刚,于增慧,曹立华,杨荣民,张晓东.三峡工程一期蓄水后长江口及其邻近海域悬浮物浓度分布特征[J].环境科学,2007,28(8):1655-1661.
[24]卢海建,翟世奎,许淑梅.三峡工程一期蓄水后长江口及其邻近海域沉积地球化学研究[J].海洋地质与第四纪地质,2006,26(5):11-19.
[25]姚家奕.多维数据分析原理与应用.清华大学出版社,2004. 5
[26]周根贵.数据仓库与数据挖掘.浙江大学出版社,2004.
[27]【美】Ralph Kimball,【美】Margy Ross.数据仓库工具箱:维度建模的完全指南.电子工业出版社,2003
[28]邵晓巍,赵长安,赵景珠,邵长胜.数据仓库的多维数据模型研究[J].计算机工程与应用,2004(11):4-8.
[29] W.H.Inmon. Building the Data Warehouse. 2nd edition. John Wiley and Sons,2000. pp.20-44.
[30] Daniel R. Dolk.Integrated model management in the data warehouse era. European Journal of Operational Research. 2000. Vo1.122, No.2.pp. 199-218
[31] Bala Iyer,G. Shankaranarayanan,Melanie L. Lenard. Model management decision environment:a Web service prototype for spreadsheet models. Decision Support Systems. 2005, Vo1.40, No.2.pp.283-304
[32] Blanning R.H. A Relational Framework for Join Implementation in Model
[33] Management Systems. Decision Support Systems. 1985.Vo1.1, No.l.pp.69-81
[34] Basu A. R.W.Blanning. Model Integration Using Metagraphs. Information SystemResearch. 1994.Vo1.5,No.3.pp.195-218
[35] Basu A. R.W.Blanning. The Analysis of Assumptions in Model Bases Using Metagraphs. Management Science. 1998.Vo1.44,No.7.pp.982-995
[36] Roger Alan Pick, Gary Klein. Model management as a component of a knowledge management system:Capturing modeling knowledge in the enterprise. Preceeding of the Eighth Americas Conference on Information Systems.Dallas,USA.2002
[37] Bhargava H.K.,R. Krishnan, R.Muller. Decision Support on Demand: Emerging Electronic Markets for Dedision Technologies. Decision Support Systems. 1997. Vo1.19, No.3.pp. 193-214
[38] Kaushal Chari. Model Composition Using Filter Spaces. Information Systems Research. 2002. Vo1.13, No.1.pp.15-35
[39] Keun-Woo Lee, Soon-Young Huh. A model-solver integration framework for autonomous and intelligent model solution. Decision Support Systems. 2006, Vol.42, No.2. pp.926-944
[40] Kingsley Gnanendran, R.P. Sundarraj. Alternative model representations and computing capacity: Implications for model management, Decision Support Systems, 2006, Vo1.42, No.3. pp.1413-430
[41] E.F.Codd,S.B.Codd, C.T.Salley. Providing OLAP to User-Analysis:an IT Mandate. White Paper. Arbor Software Corporation, 1993.
[42] Core. A. the Digital Earth:understanding our place in the 21st century [J]. The Australian Surveyor,1998,43(2):89-91.
[43] Han J,Kamber M. Data Mining Concept and Techniques [M]. Morgan Kaufmann Publishers. 2001.
[44] Wooley B,Bridges S,Hodges Jetal. Scaling the Data Mining Step in Knowledge Discovery Using Oceanographic Data [M]. IEA/AIE 2000:85-92.
[45] Guo D,Evangelinos C,Patrikalakis N. Flow Feature Extraction in Oceangraphic Visualization [M]. Computer Graphics International 2004: 162-173.
[46] Jeusfeld M.A., T.X. Bui. Distributed Decision Support and Organizational Connectivity. Decision Support Systems. 1997. Vo1.19, No.3. pp.215-225
[47] Liang T. Development of a Knowledge-based Model Mnagement system. Operational Research. 1988. Vo1.36, No.6. pp.849-863
[48] Dutta A. A.Basu. An Artificial Intelligence Approach to Model Management in Decision Support Systems. IEEE Computer. 1984. Vo1.17,No.9. pp.89-97
[49] Shaw M.J., P. Tu, P. De. Applying Machine Learning to Model Mangement in Decision Support Systems. Decision Support Systems. 1988. No.4. pp.285-305
[50] Holocher M.R. Michalski, D.Solte, F. Vicna. MIDA: An open Systems Architecture for Model-Oriented Integration of Data and Algorithms. Decision Support Systems. 1997. Vo1.20, No.2. pp.135-147
[51] Ba S., K.R. Lang, A.B. Whinston. Enterprise Decision Support Using Intranet Technology. Decsion Support Systems.1997.Vo1.20, No.2. pp.99-134
[52] Agrawal R, Gupta A, Sarawagi S. Modeling multidimensional databases. Proceedings of the 13th International Conference on Data Engineering. Los Alamitos, CA: IEEE Society Press. 1997.pp.105-I 16
[53] Muhanna W.A. On the Organization of Large Shared Model Bases. Annal Operational Research. 1992. No.38. pp.359-396
[54] Ding Q,Perrizo W. Association Rule Mining on Remotely Sensed Images Using P-tree [M]. PAKDD 2002:66-79.
[55] Kitamoto A. Data Mining for Typhoon Image Collection [M]. MDM/KDD 2001:68-77.
[56] Kitamoto A. Evolution Map:Modeling State Transition of Typhoon Image Sequences by Spatio-Temporal Clustering [M]. Discovery Science,2002: 283-290.
[57] Kitamoto A. Typhoon Analysis and Data Mining with Kernel Methods [M]. SVM 2002:237-248.
[58] Rafanelli M, Shoshani A. STORM: a Statistical Object Representation Model. Data Engineering Bulletion. 1990. Vo1.13, No.3. pp.12-18
[59] Gyssens M, Lakshmanan L V S. A Foundation for Multi-dimensional Databases. Proceedings of the 23rd Conference on very large data bases. San Francisco, CA: Morgan Kaufmann Publishers, Inc. 1997. pp.106-115
[60] Li C, Wang X S. A Data Model for Supporting On-line Analytical Processing. Proceedings of the 5th International Conference on Information and Knowledge Management. New York: Springer-Verlag, 1996. pp.81-88
[61] Lehner W. Modeling Large Scale OLAP Scenarios. Proceedings of the 6th International Conference on Extending Database Technology. New York: Springer-Verlag.1998. pp.153-167
[62] Datta A, Thomas H, A Conceptual Model and Algebra for On-line Analytical Processing in Decision Support Databases. Proceedings of the 7th Annual Workshop on Information Technologies and Systems. San Mateo, CA: Morgan Kaufman Publishers, Inc.1997. pp.91-100
[63] Pedersen T B, Jensen C S, Multidimensional Data Modeling for complex data. Proceedings of the 15th International Conference on Data Engineering. Los Alamitos, CA: IEEE Society Press. 1999. pp.336-345
[64] Gray J, Bosworth A, Layman A et al. Data Cube: A Relational Aggregation Operator Generalizing Group-bv. Cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1997. Vol.l No. l.pp.29-54
[65] R.Kimbal, L.Reeves, M.Ross, W.T.Waite, The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses. John Wiley&Sons, 1998.
[66] V. Harinarayan, A. Rajaraman, J. Ullman. Implementing Data Cubes Efficiently. In: Proc. of the 1996 ACM SIGMOD Int'1 Conf. on Management of Data, ACM Press, 1996.pp.205-216.
[67] W.S.Yang, Y.D.Chung, M.H.Kim.The RD-Tree: a structure for processing Partial-MAX/ MIN queries in OLAP. Information Sciences, 2002. Vol. 146, No.4. pp.137-149.
[68] C.T. Ho, R. Agrawal, N. Megiddo, R. Srikant. Range Queries in OLAP Data Cubes. In: Proc. of the ACM SIGMOD Conf. on the Management of Data, 1997, pp.73-88.
[69] H. Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1989.
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