基于数据仓库和SOA的地学数据集成与应用的关键技术研究
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摘要
本文将地学领域迫切希望将多源、异构、分散、多维的地学数据集成起来并提供应用服务作为研究对象,深入研究了实现地学空间数据集成与应用集成两个层面的相关技术。主要包括地学空间数据仓库、地学数据ETL、一体化存储的地学数据模型映射、多维地学空间数据立方体、SOA框架、服务模型映射、空间数据与属性数据有机集成、基于知识的岩性判别和蒸发岩沉积韵律旋回、基于服务的共享机制优化、空间数据分析等。本文的主要贡献有如下五个方面:
1.首次系统化地提出了从数据集成到应用集成、地学数据处理的一套完整的地学空间数据集成与应用集成框架。
2.提出了囊括全国地学数据的地学空间数据仓库构建模型与方法,通过集中和分布并存的模式解决海量数据访问与存储问题,并给出了具体的地学空间数据模型映射及转换规则等。设计并初步实现了地学空间ETL工具。
3.提出了带有空间维和空间度量的地学空间数据立方体,并给出了地学空间数据立方体的OLAP基本操作。
4.提出了基于SOA的地学应用集成框架,给出了属性、空间、内容、栅格等各种类型数据的集成与交互操作的完整模式。实现了矿产资源潜力、矿产地、重砂、地质图等数据库与油气、可供性分析等地学数据库的有机集成。并提出了基于重构Web服务调用返回结果和Socket服务器的两种新型优化的集成模式。
5.提出蒸发岩沉积旋徊的模式并具体应用于基于知识的蒸发岩岩性判别系统中。
研究成果在“金土工程”等项目中具体应用,达到了预期效果。
There are more than 100 kinds of national basic geological databases in China now, and capacity of data exceeds 100TB.Those databases show "Information island" phenomenon and some disadvantages:multi～source,heter-ogeneous,disperse,tightly-coupling, different semantics and reuse poorly. If geoscience data can be integrated together, then they can reserve or increase their value and service for the prospecting, mineral prospecting, resource forecast and sustainable development better.
This paper researches deeply and discusses widely in data modeling, data warehouse, application integration and share, and make some spatial analysis and data mining on the integrated data in geoscience field. It is first time to build systematically such a whole framework that about geoscience data from integration, application and analysis to one-stop service.
In order to meet the requirements of geoscience data integration and application integration, an architecture is designed to integrate and share geoscience data and application that includes data source, spatial data transformation, geoscience spatial data storage, spatial application service and one-stop client application. The geoscience spatial data integration flow chart displays tow level integration, one level is to implement the same structure data integration by using data warehouse, another level is to implement the heterogeneous data integration by using SOA that includes the same structure data integration and application integration. For ensuring long-term, complex and systematic geoscience data integration smoothly, a bottom-to-up execution pyramid pattern gives a good overall planning and milestone step-by-step method. A storage and access solution that combines centralization and distribution is used to manage geoscience data and geoscience metadata based on common warehouse metadata model and then give the physical deployment architecture for them.
This paper designs the geoscience spatial data warehouse (GSDW) model based on grid storage to storage the massive geoscience data uniformly. GSDW is composed of national geoscience primitive data warehouse, national geoscience spatial data warehouse, georaster data warehouse and content data warehouse. The data in the GSDW is organized by specific. The subset of GSDW can generate many data marts, such as geological map. A content data warehouse can organize all content data in GSDW. Full-text search can be used to select and use content service interface to construct relationship with other spatial data and property data. Because the capacity of the raster is very big, so it will consume a great deal of disk space and it maybe affect the speed of storage and select. The new generation compression technology of optimized wavelet algorithm can compress the raster data better than before. Users can't feel the delay generated by compression and decompression. Colored raster can be compressed to 3 percent of origin data, but black and white raster can save 90 percent storage space, but no loss in vision.
The rule database has enough geoscience data extract rules. The rules can limit the geoscience data those should enter the data warehouse flexible. For guaranteeing the quality of geoscience data entering data warehouse, the data have to be accepted data cleanse strictly. The rules of data cleanse can filter and modify the origin data with problems, and then ensure the quality and correctness of the geoscience decision in the future.
Modeling with the object-oriented feature of Oracle database, and vector spatial data is stored in the field whose type is SDO_GEOMETRY, raster spatial data is stored in the field whose type is SDO_GEORASTER, property data is stored in the field whose type is regular object type(such as AddressType). This paper lists a lot of mapping principle diagram that mapping from source data model to destination data model, such as one to one, one to many, many to many, and so on. They offer the templates transforming from heterogeneous to the same structure. Using identification data of the placer sample to authenticate the performance of batch loading much better than a single record loading one time, the result displays an average of 30 times faster. In order to meet the requirement of the complex geoscience data mapping, transformation and load, this paper designs the GeoSpatialETL model based on UML, and then the ETL tool is implemented by object-oriented programming language based on MVC (Mode-View-Controller) model. After analysis of the features of GSDW, parallel development pattern of GSDW with feedback as the effective pattern is used to build GSDW.
On the basis of analyzing spatial data cube principle, this paper builds the geoscience spatial data cube that with spatial dimension and spatial measure, and then users can observe and analyze the geoscience data in multi-angle. Using oil mine resource data to perform the Geoscience OLAP analysis operation, such as roll-up, drill-down, and so on.
The geoscience application integration framework based on SOA is built to meet the requirement of integrating existing components and new components after systematic introduction of the framework of SOA, SOA has some important components such as Web Service Manager, Enterprise Service Bus, Business Process Execution Language, Business Activity Monitoring, etc.The geoscience data manipulation platform is built based on SOA and using XML Web Services as main technology means, it is language independent, cross-platform, cross-database and loosely coupled. This paper does not only give the component model of geoscience service, but also give the following patterns: the mapping process of component mapping to service, the property data integration pattern based on SOA, the spatial data integration model based on GML & SOA, the integration pattern based on same structure GIS data, and the integration pattern based on heterogeneous GIS data, the access geoscience spatial data pattern based on Web Feature Service, the sharing pattern based on Catalog Service for the Web, the comprehensive geoscience data service based on SOA(the spatial, property, raster and content work in collaboration). Otherwise, a full-database search component is implemented to search some key words in the whole database and give the most detailed and most correct result.
The return data that the result of call Web Service is XML format, its capacity is huge. It can improve select speed and reduce the pressure of server if the result in XML format can be optimized. This paper gives tow optimized new share pattern. The first is to refactor the find result of Web Service. It includes only the data structure information once in the head of the XML file and the following is the real data without XML data structure. The refactoring data is compressed before sent out from the server side, decompressed and restructured on the client side before display in the window. The test result shows that the capacity of data can be reduced to the original XML file one of the 120 points. The second new model of service share is to use socket server. In this model multi-thread, queue, file buffer pool and compression technologies can improve greatly the performance of the massive data access.
After the integration of geological data analysis and data mining can find the valuable pattern or law hide the massive geoscience data. On the basic principles of spatial analysis carried out on basic, and are given the basic algorithm of buffer analysis and overlap analysis. Application of the traditional factor analysis and cluster analysis of the Dexing Copper Mine River collected samples were analyzed, and the environmental situation is the objective of the comprehensive evaluation. The initial evaporation of discrimination rock model is constructed in this paper, set up a series of evaporation rock of discrimination rules. The evaporation rock lithology discrimination is performed based on the knowledge, found that the evaporation sedimentary rock rhythm cycle model, and apply it in lithology discrimination process and effective solution to the problem of multiple solutions lithology, the result was more satisfactory than before.
The initial realization of the data integration and application integration, data includes the potential of the mineral resources, mine deposit, the basic geography, the geological maps, and so on. The object-oriented type of Oracle is used to establish the core data model of the data warehouse. Tow different SOA servers are called by all kinds of clients to achieve the same structure and heterogeneous data sharing and application sharing. The structure of the geological integrated system based on MVC model is constructed and an integrated platform is established. At last it shows some diagrams, they are national oil resource overlap analysis result, national coal resource buffer analysis, the seamless integrated search result of potential database and mine database, the integrated analysis result of supply analysis system, the integrated result of national oil and gas database, the integrated result of evaporation rock of discrimination system, etc. Combination of geological background, oil and coal resource potential summed up its distribution, and the usage and exploitation of the availability of an analysis and forecast in the future.
Through the "golden soil project" project in the realization and application, the result shows that the research results of this paper achieved the expected goals of geological data integration and application integration. It has a great practical significance and provides a valuable reference in the geological field for the data integration and application integration in the future.
1.首次系统化地提出了从数据集成到应用集成、地学数据处理的一套完整的地学空间数据集成与应用集成框架。
2.提出了囊括全国地学数据的地学空间数据仓库构建模型与方法,通过集中和分布并存的模式解决海量数据访问与存储问题,并给出了具体的地学空间数据模型映射及转换规则等。设计并初步实现了地学空间ETL工具。
3.提出了带有空间维和空间度量的地学空间数据立方体,并给出了地学空间数据立方体的OLAP基本操作。
4.提出了基于SOA的地学应用集成框架,给出了属性、空间、内容、栅格等各种类型数据的集成与交互操作的完整模式。实现了矿产资源潜力、矿产地、重砂、地质图等数据库与油气、可供性分析等地学数据库的有机集成。并提出了基于重构Web服务调用返回结果和Socket服务器的两种新型优化的集成模式。
5.提出蒸发岩沉积旋徊的模式并具体应用于基于知识的蒸发岩岩性判别系统中。
研究成果在“金土工程”等项目中具体应用,达到了预期效果。
There are more than 100 kinds of national basic geological databases in China now, and capacity of data exceeds 100TB.Those databases show "Information island" phenomenon and some disadvantages:multi～source,heter-ogeneous,disperse,tightly-coupling, different semantics and reuse poorly. If geoscience data can be integrated together, then they can reserve or increase their value and service for the prospecting, mineral prospecting, resource forecast and sustainable development better.
This paper researches deeply and discusses widely in data modeling, data warehouse, application integration and share, and make some spatial analysis and data mining on the integrated data in geoscience field. It is first time to build systematically such a whole framework that about geoscience data from integration, application and analysis to one-stop service.
In order to meet the requirements of geoscience data integration and application integration, an architecture is designed to integrate and share geoscience data and application that includes data source, spatial data transformation, geoscience spatial data storage, spatial application service and one-stop client application. The geoscience spatial data integration flow chart displays tow level integration, one level is to implement the same structure data integration by using data warehouse, another level is to implement the heterogeneous data integration by using SOA that includes the same structure data integration and application integration. For ensuring long-term, complex and systematic geoscience data integration smoothly, a bottom-to-up execution pyramid pattern gives a good overall planning and milestone step-by-step method. A storage and access solution that combines centralization and distribution is used to manage geoscience data and geoscience metadata based on common warehouse metadata model and then give the physical deployment architecture for them.
This paper designs the geoscience spatial data warehouse (GSDW) model based on grid storage to storage the massive geoscience data uniformly. GSDW is composed of national geoscience primitive data warehouse, national geoscience spatial data warehouse, georaster data warehouse and content data warehouse. The data in the GSDW is organized by specific. The subset of GSDW can generate many data marts, such as geological map. A content data warehouse can organize all content data in GSDW. Full-text search can be used to select and use content service interface to construct relationship with other spatial data and property data. Because the capacity of the raster is very big, so it will consume a great deal of disk space and it maybe affect the speed of storage and select. The new generation compression technology of optimized wavelet algorithm can compress the raster data better than before. Users can't feel the delay generated by compression and decompression. Colored raster can be compressed to 3 percent of origin data, but black and white raster can save 90 percent storage space, but no loss in vision.
The rule database has enough geoscience data extract rules. The rules can limit the geoscience data those should enter the data warehouse flexible. For guaranteeing the quality of geoscience data entering data warehouse, the data have to be accepted data cleanse strictly. The rules of data cleanse can filter and modify the origin data with problems, and then ensure the quality and correctness of the geoscience decision in the future.
Modeling with the object-oriented feature of Oracle database, and vector spatial data is stored in the field whose type is SDO_GEOMETRY, raster spatial data is stored in the field whose type is SDO_GEORASTER, property data is stored in the field whose type is regular object type(such as AddressType). This paper lists a lot of mapping principle diagram that mapping from source data model to destination data model, such as one to one, one to many, many to many, and so on. They offer the templates transforming from heterogeneous to the same structure. Using identification data of the placer sample to authenticate the performance of batch loading much better than a single record loading one time, the result displays an average of 30 times faster. In order to meet the requirement of the complex geoscience data mapping, transformation and load, this paper designs the GeoSpatialETL model based on UML, and then the ETL tool is implemented by object-oriented programming language based on MVC (Mode-View-Controller) model. After analysis of the features of GSDW, parallel development pattern of GSDW with feedback as the effective pattern is used to build GSDW.
On the basis of analyzing spatial data cube principle, this paper builds the geoscience spatial data cube that with spatial dimension and spatial measure, and then users can observe and analyze the geoscience data in multi-angle. Using oil mine resource data to perform the Geoscience OLAP analysis operation, such as roll-up, drill-down, and so on.
The geoscience application integration framework based on SOA is built to meet the requirement of integrating existing components and new components after systematic introduction of the framework of SOA, SOA has some important components such as Web Service Manager, Enterprise Service Bus, Business Process Execution Language, Business Activity Monitoring, etc.The geoscience data manipulation platform is built based on SOA and using XML Web Services as main technology means, it is language independent, cross-platform, cross-database and loosely coupled. This paper does not only give the component model of geoscience service, but also give the following patterns: the mapping process of component mapping to service, the property data integration pattern based on SOA, the spatial data integration model based on GML & SOA, the integration pattern based on same structure GIS data, and the integration pattern based on heterogeneous GIS data, the access geoscience spatial data pattern based on Web Feature Service, the sharing pattern based on Catalog Service for the Web, the comprehensive geoscience data service based on SOA(the spatial, property, raster and content work in collaboration). Otherwise, a full-database search component is implemented to search some key words in the whole database and give the most detailed and most correct result.
The return data that the result of call Web Service is XML format, its capacity is huge. It can improve select speed and reduce the pressure of server if the result in XML format can be optimized. This paper gives tow optimized new share pattern. The first is to refactor the find result of Web Service. It includes only the data structure information once in the head of the XML file and the following is the real data without XML data structure. The refactoring data is compressed before sent out from the server side, decompressed and restructured on the client side before display in the window. The test result shows that the capacity of data can be reduced to the original XML file one of the 120 points. The second new model of service share is to use socket server. In this model multi-thread, queue, file buffer pool and compression technologies can improve greatly the performance of the massive data access.
After the integration of geological data analysis and data mining can find the valuable pattern or law hide the massive geoscience data. On the basic principles of spatial analysis carried out on basic, and are given the basic algorithm of buffer analysis and overlap analysis. Application of the traditional factor analysis and cluster analysis of the Dexing Copper Mine River collected samples were analyzed, and the environmental situation is the objective of the comprehensive evaluation. The initial evaporation of discrimination rock model is constructed in this paper, set up a series of evaporation rock of discrimination rules. The evaporation rock lithology discrimination is performed based on the knowledge, found that the evaporation sedimentary rock rhythm cycle model, and apply it in lithology discrimination process and effective solution to the problem of multiple solutions lithology, the result was more satisfactory than before.
The initial realization of the data integration and application integration, data includes the potential of the mineral resources, mine deposit, the basic geography, the geological maps, and so on. The object-oriented type of Oracle is used to establish the core data model of the data warehouse. Tow different SOA servers are called by all kinds of clients to achieve the same structure and heterogeneous data sharing and application sharing. The structure of the geological integrated system based on MVC model is constructed and an integrated platform is established. At last it shows some diagrams, they are national oil resource overlap analysis result, national coal resource buffer analysis, the seamless integrated search result of potential database and mine database, the integrated analysis result of supply analysis system, the integrated result of national oil and gas database, the integrated result of evaporation rock of discrimination system, etc. Combination of geological background, oil and coal resource potential summed up its distribution, and the usage and exploitation of the availability of an analysis and forecast in the future.
Through the "golden soil project" project in the realization and application, the result shows that the research results of this paper achieved the expected goals of geological data integration and application integration. It has a great practical significance and provides a valuable reference in the geological field for the data integration and application integration in the future.
引文
[1]牛文元.新时期中国地质工作发展的六大战略要点[J].地质通报.2003,22:850-853.
[2]方克定.不同时空条件的地质工作与经济发展需求[J].地质通报.2003,22:845-849.
[3]姜作勤,马智民.地质信息服务体系研究报告.中国地质调查局发展研究中心,2006,1-10.
[4]李振华、胡光道,王淑华.一个地学数据仓库的初步设计与实现[J].地质与勘探,2002,38(5):67-70.
[5]胡光道.地质数据仓库设计中的几个问题[J].地球科学-中国地质大学学报,1999,5:522-524.
[6]李德仁,王树良,李德毅.空间数据挖掘理论与应用[M],北京:科学出版社,2006,19-44.
[7]杨毅恒,韩燕.多维地学数据处理技术与方法[M].北京:科学出版社,2002,30-68.
[8]肖克炎,王勇毅,陈郑辉等.中国矿产资源评价新技术与评价新模型[M].北京:地质出版社,2006,74-86、177-209.
[9]中国地质调查局发展研究中心,GeoExpl使用手册,2003。
[10]王晓明,高勇,刘玉玲.面向水环境管理的空间数据仓库构建[J].计算机应用研究,2005,11:195-197.
[11]朱庆,周艳.分布式空间数据存储对象[J].武汉大学学报信息科学版,2006,31(5):391-394.
[12]王永志,杨毅恒,高光大,陈苗.地学空间数据仓库构建技术[J].地质通报,2008,27(5):713-719.
[13]陈苗,杨毅恒,王永志.表分区在优化海量地质数据检索中的应用[J].世界地质,2008,27(1):100-104。
[14]崔晓军,薛永生.数据仓库集成环境研究与实现[J].计算机应用研究,2006,12:178-184.
[15]赵晓非,黄志球.基于CWM的元数据集成中形式化推理技术的研究[J].计算机科学,2006,33(12):177-182.
[16]王宁,王延章,于淼.面向一般决策过程的数据仓库系统研究[J].计算机集成制造系统,2006,12(1):139-143.
[17]李海波,王丽珍,杨莉.统一空间数据仓库权限管理分析与设计[J].计算机工程,2006,32(19):54-57.
[18]韦波,李景文.基于Oracle 10g拓扑数据模型的空间管网信息系统WebGIS实现[J].计算机应用,2006,26:115-116,121.
[19]姚力波,王仁礼.基于Oracle Spatial空间数据库的GIS数据管理[J].测绘与空间地理信息,2006,29(2):82-86.
[20]魏东,陈晓江,房鼎益.基于SOA体系结构的软件开发方法研究[J].微电子学与计算机,2005,22(6):73-75.
[21]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):13-16.
[22]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003,24-31.
[23]赵文兵,廖湖声,谢昆青.基于GML的地理信息集成系统研究[J].计算机工程与应用,2006,48-48,53.
[24]旷建中,马劲松,蒋民锋.基于GML的多源空间数据集成模型研究[J].计算机应用研究,2005,6:105-107.
[25]李景朝,王永志.我国石油、煤炭、铁矿、钾盐矿产资源潜力数据库建设项目成果报告.中国地质调查局发展研究中心,2008,25-55.
[26]赵鹏大,矿产勘查理论与方法[M].武汉:中国地质大学出版社.2001,88-120.
[27]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003:24-31.
[28]陈丹,袁捷.基于SOA的分布式科研信息系统[J].计算机工程与设计,2006,27(24):4756-4761,4766.
[29]苏炳均,诸昌钤,李林.基于Oracle Spatial的GIS数据组织及查询[J].计算机应用研究,2006,12:278-280.
[30]彭明军,李宗华.基于Oracle Spatial的空间数据互操作[J].计算机工程与应用,2006,32:154-157.
[31]聂独,李晓明,田雪,等.基于Oracle Spatial的配电网GIS数据存储方法[J].2006,26(5):42-45.
[32]王永志,杨毅恒,路来君.基于SOA的油气资源评价系统设计与实现[J].世界地质,2007,26(4):447-452.
[33]王永志,杨毅恒,初娜,赵元艺.德兴铜矿大坞河流域土壤Cu元素形态的聚类分析[J],地球物理学进展,2008,23(1):233-236。
[34]陈苗,杨毅恒,王永志.基于SOA的蒸发岩盆地岩性判别系统[J],地球物理学进展,2008,23(2):528-532.
[35]吴文明,瞿裕忠,董逸生.Web服务及相关技术[J].计算机应用与软件,2004,21(3):14-15.
[36]吴勇.一站式SOA解决方案[R].Oracle Corporation,2007,6-60.
[37]刘冰,卢秀山,田茂义.基于SOA的空间数据管理研究[J].测绘科学,2007,32(1):124-25.
[38]罗英伟,汪小林,许卓群.层次化WebGIS构件系统的设计与实现[J].计算机学报,2004,27(2):177-185.
[39]何牧,孙中轶,蔡鸿明,等.基于网格向SOA提供资源的设计和实现[J].计算机工程,2006,32(21):110-112.
[40]杜攀,徐进.SOA体系下细粒度组件服务整合的探讨[J].计算机应用,2006,26(3):699-702.
[41]桂小林,王庆江,龚文强,等.面向网格计算的机器选择算法研究[J].计算机研究与发展,2004,41(12):2189-2194.
[42]刘湘南,黄方等,GIS空间分析原理与方法[M].北京:科学出版社,2006,138-140.
[43]汤国安、杨昕,ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社,2006,196-233.
[44]李裕伟,赵精满,李晨阳.基于GMS、DSS和GIS的潜在矿产资源评价方法(上下册)[M].北京:地震出版社,2007,101-152.
[45]张发旺,侯新伟,韩占涛.煤炭开发引起水土环境演化及其调控技术[J].地球学报,2001,22(4):345-350.
[46]王文雯,赵彦玲.济南市玉绣河沿岸绿地的生物多样性分析及其优化措施[J].地 球物理学进展,2005,2(2):482-486.
[47]孙扬,王跃思,李昕,等.北京地区一次持续重污染过程O3、NOx、CO的垂直分布分析[J].地球物理学报,2006,49(6):1616-1622
[48]赵元艺,张光第,蔡剑辉.德兴铜矿床地质模型初探[J].西北地质,2003,36增刊:126-129.
[49]郭秀军,武瑞锁,贾永刚,等.不同土壤中含油污水污染区的电性变化研究及污染区探测[J].地球物理学进展,2005,20(2):402-406.
[50]汤懋苍,张拥军,李栋梁.青藏铁路沿线的天灾及其预测方法探索[J].地球物理学报,2006,49(5):1316-1320.
[51]刘熙明,胡非,李磊等.北京地区夏季城市气候趋势和环境效应的分析研究[J].地球物理学报,2006,49(3):689-697.
[52]李婧,刘树华,茅宁豪等.不同生态系统CO2通量和浓度特征分析研究[J].地球物理学报,2006,49(5):1298-1307.
[53]党志,刘丛强,尚爱安.矿区土壤中重金属活动性评估方法的研究进展[J].地球科学进展,2001,16(1):86-92.
[54]李宇庆,陈玲,仇雁翎.上海化学工业区土壤重金属元素形态分析[J].生态环境,2004,13(2):154-155.
[55]何丽莉,白洪涛.用聚类分析方法挖掘Aspect[J].计算机集成制造系统,2006,12(1):149-153.
[56]杨毅恒,韩燕,徐兵,等.多维地学数据处理技术与方法[M].北京:科学出版社,2002,56-79.
[57]刘书暖,张振明,田锡天等.基于聚类分析法的典型工艺路线发现方法[J].计算机集成制造系统,2006,12(7):996-1001.
[58]闫伟,张浩,陆剑峰,等.聚类分析理论研究及在流程企业中的应用[J].计算机工程,2006,32(17):19-21,27.
[59]殷晓曦,许光泉,桂和荣,等.系统聚类逐步判别法对皖北矿区突水水源的分析[J].煤田地质与勘探,2006,32(2):58-61.
[60]李树军,纪宏金.对应聚类分析与变量选择[J].地球物理学进展,2005,20(3):694-697.
[61]闫海涛,胡守云,Appel E,等.土壤剖面中粉煤灰垂向迁移的磁响应[J].地球物理学报,2005,48(6):1392-1399
[62]初娜,赵元艺,张光弟,等.德兴铜矿低品位矿石堆浸场与大坞河流域土壤重金属元素形态的环境特征[J].地质学报,2007,81(5):670-683.
[63]贾承造,魏国齐.“九五”期间塔里木盆地构造研究成果概述[J].石油勘探与开发,2003:30(1):11-14.
[64]李勇,钟建华,温志峰,段宏亮,王海侨.蒸发岩与油气生成、保存的关系[J].沉积学报,2006,24(4):596-606.
[65]张辉,高德利.钻井岩性实时识别方法研究[J].石油钻采工艺,2005,27(1):13-15,80.
[66]刘秀娟,陈超,曾冲,兰磊.利用测井数据进行岩性识别的多元统计方法[J].地质科技情报,2007,26(3):109-112.
[67]原宏壮,陆大卫,张辛耘,等.测井技术新进展综述[J]地球物理学进展,2005,20(3):786-795.
[68]徐海浪,吴小平.电阻率二维神经网络反演[J].地球物理学报,2006,49(2):584-589.
[69]曹正良,王克协,谢荣华,马在田.三种阵列声波测井数据频散分析方法的应用与比较[J].地球物理学报,2005,48(6):1449-1459.
[70]邓小波,聂在平,赵延文,等.用相位感应测井数据反演地层电阻率和介电常数.地球物理学报,2006,49(2):604-608
[71]石油化学工业部化学矿山局,石油勘探中找钾盐矿的方法[M].北京:石油工业出版社,1977.
[72]陈一鸣,朱德怀,任康,等.矿场地球物理测井技术测井资料解释[M].北京:石油工业出版社,1994,120-130.
[73]李新虎.基于不同测井曲线参数集的支持向量机岩性识别对比[J].煤田地质与勘探,2007,35(3):72-76,80.
[74]毛新生.SOA原理、方法、实践[M].电子工业出版社,2007,63-86.
[75]吴庆岩,张爱军译。测井解释常用岩石矿物手册[M].石油工业出版社,1998,10-88。
[76]叶舟,王东.基于规则引擎的数据清洗[J],计算机工程,2006,32(23):52-54.
[77]黎建辉,吴威,阎保平.一种基于XML的元数据映射与转换方法[J],微电子学与计算机,2008,25(1):34-38.
[78]王韦伟,孙庆鸿.基于XML的分布异构数据集成平台[J],2006,36(5):715-719.
[79]叶炜,顾宁.在数据网格环境中可靠获取分布式数据的方法[J].通信学报,2006,27(11):119-124.
[80]唐宇,何凯涛,肖侬,张永波,景宁.国家地质调查应用网格体系及关键技术研究[J].计算机研究与发展,2003,40(12):1682-1683.
[81]赵宏,刘鹏.数据网格建设与系统集成[J].计算机工程与设计,2006,27(23):4424-4426.
[82]王珊,张坤龙.网格环境下的数据库系统[J].计算机应用,2004,24(10):1-3,23.
[83]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003,70-102.
[84]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):339-341.
[85]邹逸江.面向“数字城市”多源数据的空间数据立方体空间度量研究[J].地理与地理信息科学,2007,23(2):22-25.
[86]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.
[87]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,2007,2199-202.
[98]邹逸江.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.
[89]裴蕾,陶树平.在数据仓库中如何有效地实现数据立方体的计算[J].2005,8:42-45.
(?)徐焱,孙扬.Oracle XSQL技术[M].北京:清华大学出版社,2004,41-53.
(?)赵元艺,王金生.矿床地质环境模型与环境评价[M].北京:地质出版社,2007,(?)7.
(?)来君,矿产资源评价与管理系统研制[],批准登记号2005142,吉林省科技厅。
(?)来君,陆林生,魏远泰.河北地勘局多元地学信息系统研制项目报告[M].1999,
[94]刘成林,塔里木盆地重要蒸发岩坳陷成盐及油气生储条件研究设计书(41005050002),2005-2008,2-10.
[95]李德仁.论空间数据挖掘和知识发现[J].武汉大学学报 信息科学版,2001,26(6):491-499.
[96]李德仁.空间数据挖掘和知识发现理论与方法的研究[J].武汉大学学报 信息科学版,2002,27(3):221-233.
[97]李德仁,关泽群.空间信息系统的集成与实现[M].武汉:武汉大学出版社,2000,92-118.
[98]王树良.基于数据场与云模型的空间数据挖掘和知识发现.武汉:武汉大学,2002,30-42.
[99]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.
[100]于焕菊,谢传节,李云岭,秦承志.中国华北地区地震空间数据仓库的构建与分析[J].地球信息科学,2006,8(3):88-93.
[101]邹逸江.空间数据仓库的概念框架和认知过程[J].计算机应用研究,2007,24(5):186-189.
[102]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.
[103]王紫瑶,南俊杰,段紫辉,等.SOA核心技术及应用[M].北京:电子工业出版社,2008,107-122.
[104]梁爱虎.精通SOA:基于服务总线的Struts+EJB+Web Services整合应用开发.北京:电子工业出版社,2007,227-232.
[105]杨涛,刘锦德.Web Services技术综述一种面向服务的分布式计算模式[J].计算机应用,2004,24(8):1-4.
[106]任捷,吴明晖,应晶.Web Services技术在异构系统集成中的应用研究[J].计算机应用,2004,24(1):95-98.
[107]袁占亭,张秋余,杨洁.基于Web Services的企业应用集成解决方案研究[J].计算机集成制造系统-CIMS,2004,10(4):394-398,414.
[108]王海起,王劲峰.空间数据挖掘技术研究进展[J].地理与地理信息科学,2005,21(4):6-10.
[109]孙庆先,方涛,郭达志.空间数据挖掘中的尺度转换研究[J].计算机工程与应用,2005,16:17-19.
[110]吴亮,谢忠.基于GML的地质图空间数据库交换体系[J].地球科学-中国地质大学学报,2006,31(5):650-652.
[111]兰小机,张书亮,刘德儿,等.GML空间数据库系统研究[J].测绘科学,2005,30(5):16-19.
[112]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务设计.中国地质调查局,2002,4-11.
[113]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务示范报告.中国地质调查局,2005,167-180.
[114]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务研究报告.中国地质调查局,2005,72-136.
[115]叶天竺.全国重要矿产资源潜力预测评价及综合项目总体设计.中国地质调查局,2006,5-12.
[116]叶天竺.全国重要矿产资源潜力预测评价及综合项目2007年总结报告.中国地 质调查局,2007,8-22.
[117]叶天竺.全国重要矿产资源潜力预测评价及综合项目数据库维护设计.中国地质调查局,2007,3-18.
[118]叶天竺.全国重要矿产资源潜力预测评价及综合项目2008年设计.中国地质调查局,2008,2-14.
[119]左群超.全国重要矿产资源潜力预测评价及综合数据模型设计.中国地质调查局,2008,4-10.
[120]杨东来,左群超.国家基础地质数据库整合与集成项目总体设计.中国地质调查局,2008,3-8.
[121]李超岭,吕霞.国土资源信息集成与共享平台建设总体设计.中国地质调查局发展研究中心,2006,4-22.
[122]李超岭,吕霞.国土资源信息集成与共享平台建设2006年度总结.中国地质调查局发展研究中心,2007,10-15.
[123]李超岭,吕霞.国土资源信息集成与共享平台建设项目2007年度总结.中国地质调查局发展研究中心,2007,22-31.
[124]李超岭,吕霞.国土资源信息集成与共享平台建设项目2008年度设计.中国地质调查局发展研究中心,2008,3-18.
[125]郎宝平,姜作勤.地质调查信息化技术支撑体系战略研究报告.中国地质调查局发展研究中心,2007,9-15.
[126]杨东来,姜作勤.国家地质工作信息化发展战略研究报告.中国地质调查局发展研究中心,2007,7-13.
[127]马智民,李霞.主要发达国家地质调查机构信息化跟踪研究报告-英国地调局.中国地质调查局发展研究中心,2007,2-15.
[128]马智民,张伟.主要发达国家地质调查机构信息化跟踪研究报告-澳大利亚地学局.中国地质调查局发展研究中心,2007,21-52.
[129]王群,张伟,王影.主要发达国家地质调查机构信息化跟踪研究报告-加拿大地调局.中国地质调查局发展研究中心,2007,9-16.
[130]马智民,张贷琼.主要发达国家地质调查机构信息化跟踪研究报告-美国地调局.中国地质调查局发展研究中心,2007,30-41.
[131]李景朝,杨东来,姜作勤.中国地质调查局地质数据资源现状与分析.中国地质调查局发展研究中心,2007,2-12.
[132]李超岭、陈辉.地质调查工作主流程信息化研究报告.中国地质调查局发展研究中心,2007,10-22.
[133]国土资源部.国土资源信息核心元数据标准.2003,1-31.
[134]谭永杰.我国石油、煤炭、铁矿、钾盐矿产资源潜力数据库建设设计书.中国地质调查局发展研究中心,2007,4-15.
[135]车长波,景东升.国家级油气资源数据库建设总体设计(初稿).国土资源部油气中心,2007,1-30.
[136]李颖,李伟东.国家级油气资源数据库建设软件设计.吉林大学,2007,2-15.
[137]张道勇.国家级油气资源数据库建设-盆地专题总体设计.国土资源部油气中心,2007,1-9.
[138]王永志.国家级油气资源数据库建设面向对象总体设计.吉林大学,2008,2-40.
[139]Foster I,Kesselman C,Tuecke S.The anatomy of the grid:enabling scalable virtual organization[J].International Journal of High Performance Computing Applications, 2001, 15 (3): 200-222.
[140]Craig Walls, Ryan Breidenbach.Spring in Action.Manning Publications, 2005, 10-33.
[141]John Beatty , Henning Blohm , et al.Service Data Objects For Java Specification.2005, http: //www.bea.com/dev2dev/assets/sdo/SDO_Specification_Java_V2.01.pdf
[142]Michael Beisiegel, Henning Blohm, et al.SCA Service Component Architecture Client and Implementation Model Specification for Java.2005 , http : //dev2dev.bea.com/2005/11/SCA_ClientAndImplementationModelforJava_V09.pdf.
[143]Gonzales M.L. Spatial OLAP: conquering geography.DB2 Magazine, 1999, 4(1): 16-20.
[144]Han J. Towards on-line analytical mining in large databases.ACM SIGMOD-Record, 1998.
[145]Zhou X., Truffer D., Han J. Efficient polygon amalgamation methods for spatial OLAP and spatial data mining.Lecture Notes in Computer Science, 1999, 1651: 167-187。
[146]Atkinson P.M., Lewis, P.Geostatistical classification for remote sensing: an introduction.Computers & Geosciences, 2000, 26: 361-371.
[147]Costa J.P., Pronzato, L., Thierry E.Nonlinear prediction by kriging with application to noise cancellation.Signal Processing, 2000, 80: 553-566.
[148]Wang J., Yang J., Muntz R.An approach to active spatial data mining based on statistical information.IEEE Transactions on Knowledge and Data Engineering, 2000, 12 (5): 715-728.
[149]Wang S.L. A try for handling uncertainties in spatial data mining.Lecture Notes in Artificial Intelligence, 2004, 3215: 513-520.
[150]Wang S.L., Yuan H.N.View-angle of spatial mining.Lecture Notes in Artificial Intelligence, 2006, 4093: 1065-1076.
[151]Tung A.Spatial clustering in the presence of obstacles.IEEE Transactions on Knowledge and Data Engineering, 2001, 359-369.
[152] J.W.Han, N.Stefanovic, K.Koperski.Selective Materialization: An efficient method for spatial data cube construction.PAKDD'98, 1998: 144-158.
[153]Lilian Hobbs, Susan Hillson, Shilpa Lawande, et al.Oracle Database 10g Data Warehousing[M].Oracle Corporation, 2005, Elsevier Digital Press.
[154] Davis Dwight B. Oracle Fusion Architecture Eases the Adoption of Service-Oriented Architecture [R], Oracle Corporation, 2006: 3-15.
[155] Pickersgill Catherine, Whitmore Robin, Bathurst William, et al. Oracle SOA Suite Developer's Guide (10g) [R]. Oracle Corporation, 2006: 1-5.
[156] Brown Alan W., Johnston Simon K., Larsen Grantl. Practice Tutorial of Using IBM Rational Software Platform to Develop SOA [R]. IBM Corporation, 2006: 5-28.
[157] Deanna Bradshaw, Mark Kennedy. Oracle BPEL process manager developer's guide [R]. Oracle Corporation, 2006: 29-33.
[158]William H.I. Building the Data Warehouse [M]. Indianapolis: Wiley Publishing, 2005, 29-33.
[159]Paul L. Oracle Data Warehousing Guide 10g Release 2[OL]. Oracle Corporation, http: //download.oracle.com/docs/cd/B19306_01/server.102/bl4223/toc.htm, 2005.
[160]Marla A. Oracle Content Database Administrator's Guide[OL]. Oracle Corporation, 2006, http: //download.oracle.com/docs/cd/B32119_01/doc/contentdb.1012/b31268.pdf.
[161] Chuck M. Oracle Spatial User's Guide and Reference 10g Release 1 [OL]. Oracle Corporation, http: //download.oracle.com/docs/html/B10826_01/toc.htm, 2003.
[162]Chuck M. Oracle Spatial GeoRaster[OL]. Oracle Corporation, 2005, http://download.oracle.com/docs/pdf/B14254_01.pdf.
[163]Ken C, Orlando C. Oracle SOA Suite Developer's Guide[OL], Oracle Corporation, 2006, http: //download.oracle.com/docs/cd/B31017_01/core.1013/b28764.pdf.
[164]Chuck M. Oracle Application Server MapViewer User's Guide[OL]. Oracle Corporation, 2005, http: //download-west.oracle.com/docs/cd/B14099_19/web.1012/b14036/toc.htm.
[165]Chuck M.Oracle Spatial GeoRaster Developer's Guide 11g Release 1 (11.1) [OL].Oracle Corporation, 2007, 58-122.
[166]Shashaanka Agrawal, Geeta Arora, Eric Belden.Oracle Database Object-Relational Developer's Guide llg Release 1 (11.1) [OL]. Oracle Corporation, 2007, 32-65.
[167]Chuck Murray. Oracle Spatial Developer's Guide 11gRelease 1 (11.1 )[OL].2007.
[168]John P, Dan C, Douglas T. Common Warehouse Metamodel Developer's Guide[M].Indianapolis: Wiley Publishing, 2003, 431-554..
[169]Jiawei Han Jenny Y. Chiang Sonny Chee. DBMiner～～A System for Data Mining in Relational Databases and Data.pdf. http: //db.cs.sfu.ca/
[170]Brian Reistad, Bryan Murray, Doug Davis, et al.Web Services Resource Transfer (WS-RT) .2006, 44-80.
[171]Demed L'Her, Khanderao Kand. What's New in Oracle SOA Suite.Oracle Corporation, 2007, http: //www.oracle.com/technology/products/ias/bpe1/techpreview/s291362-whats-new-in-oracle-soa-suite.pdf.
[172] Deanna Bradshaw, Mark Kennedy.Oracle BPEL Process Manager Developer's Guide 10g (10.1.3.1.0) [OL].Oracle Corporation, 2006, 22-29.
[173] Mark Kennedy.Oracle BPEL Process Manager Quick Start Guide 10g (10.1.3.1.0) [OL].Oracle Corporation, 2006, 43-55.
[174]Oracle. Business Activity Monitoring Datasheet[OL]. Oracle Corporation, 2006, 1-10.
[170]Harpal Kochar .Business Activity Monitoring White Paper[OL]. Oracle Corporation, 2005, 3-8.
[175]Matthew Adams, Xcalia, et al.Service Data Objects For Java Specification[OL]. Oracle Corporation, 2006, 22-32.
[176]Oracle. Oracle Web Services Manager[OL]. Oracle Corporation, 2005, 10-18.
[177]Rick Caccia.An Introduction to Oracle Web Services Manager[OL].Oracle Corporation, 2005, 24-60.
[178]Martin, Chris, Alan, et al.Patterns: Implementing an SOA Using an Enterprise Service Bus[OL].IBM Corporation, 2004, 88-104.
[179]Simon Cox, Paul Daisey. OpenGIS(?) Geography Markup Language (GML) Implementation Specification, 2004, http: //schemas.opengis.net/gml/3.1.1/base/.
[180]K Czajkowski, S Fitzgerald, I Foster et al. Grid information services for dsitributed resource sharing. In: Proc of the 10th IEEE Int'l Symp on High-Performance Distributed Computing (HPDC-10) .San Francisco: IEEE Press, 2001, 1001-1011.
[181 ]Prasad Bagal, Mark Bauer, Eric Belden.Oracle Database Storage Administrator's Guide 11g Release 1 (11.1) [OL].Oracle Corporation, 2008, 101-112.
[182]Mark Bauer, Richard Strohm.Oracle Real Application Clusters Administration and Deployment Guide 11g Release 1 (11.1) [OL]. Oracle Corporation, 2007, 77-89
[183]Ted Husted, Cedric Dumoulin, George Franciscus, David Winterfeldt.Struts in Action[M].Manning, 2003, 86-112.
[184]Lance Ashdown, Janis Greenberg, Jack Melnick.Oracle XML Developer's Kit Programmer's Guide 11g Release 1.Oracle Corporation, 2007, 25-42.
[185]Codd E.Twelve rules for on-line analytic processing. Computer world, 1995.
[186]Inmon W.H. Building the Data Warehouse (Fourth Edition) [M].Wiley, 2007, 80-123.
[187]PAPADIAS D.Efficient OLAP Operation in Spatial Data Warehouses R.HongKong: Technical Report HKUST-CS01-01, 2001, 65-69.
[188]WANGB Y, PAN F.Efficient OLAP Operations for Spatial Data Using Peano Trees C.San Die go: DMKD'03, 2003, 126-130.
[189]Ester M.Spatial data mining: databases primitives, algorithms and efficient DBMS support[J]. Data Ming and Knowledge Discovery, 2000 (4): 193-216。
[190]Kaufman L., Rousseew P.J. Finding Groups in Data: an Introduction to Cluster Analysis[M].New York: John Wiley&Sons, 1990, 58-77.
[191]Koperski K., Adhikary J., Han J.Spatial data mining:process and challenges survey paper.SIGMOD'96 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'96) .Canada, 1996.
[192]Miller H.J., Han J. Geographic Data Mining and Knowledge Discovery.London: Taylor & Francis, 2001.
[193]Dave Crane, Eric Pascarello, Darren James.Ajax in Action.Manning Publications, 2006, 10-23.
[2]方克定.不同时空条件的地质工作与经济发展需求[J].地质通报.2003,22:845-849.
[3]姜作勤,马智民.地质信息服务体系研究报告.中国地质调查局发展研究中心,2006,1-10.
[4]李振华、胡光道,王淑华.一个地学数据仓库的初步设计与实现[J].地质与勘探,2002,38(5):67-70.
[5]胡光道.地质数据仓库设计中的几个问题[J].地球科学-中国地质大学学报,1999,5:522-524.
[6]李德仁,王树良,李德毅.空间数据挖掘理论与应用[M],北京:科学出版社,2006,19-44.
[7]杨毅恒,韩燕.多维地学数据处理技术与方法[M].北京:科学出版社,2002,30-68.
[8]肖克炎,王勇毅,陈郑辉等.中国矿产资源评价新技术与评价新模型[M].北京:地质出版社,2006,74-86、177-209.
[9]中国地质调查局发展研究中心,GeoExpl使用手册,2003。
[10]王晓明,高勇,刘玉玲.面向水环境管理的空间数据仓库构建[J].计算机应用研究,2005,11:195-197.
[11]朱庆,周艳.分布式空间数据存储对象[J].武汉大学学报信息科学版,2006,31(5):391-394.
[12]王永志,杨毅恒,高光大,陈苗.地学空间数据仓库构建技术[J].地质通报,2008,27(5):713-719.
[13]陈苗,杨毅恒,王永志.表分区在优化海量地质数据检索中的应用[J].世界地质,2008,27(1):100-104。
[14]崔晓军,薛永生.数据仓库集成环境研究与实现[J].计算机应用研究,2006,12:178-184.
[15]赵晓非,黄志球.基于CWM的元数据集成中形式化推理技术的研究[J].计算机科学,2006,33(12):177-182.
[16]王宁,王延章,于淼.面向一般决策过程的数据仓库系统研究[J].计算机集成制造系统,2006,12(1):139-143.
[17]李海波,王丽珍,杨莉.统一空间数据仓库权限管理分析与设计[J].计算机工程,2006,32(19):54-57.
[18]韦波,李景文.基于Oracle 10g拓扑数据模型的空间管网信息系统WebGIS实现[J].计算机应用,2006,26:115-116,121.
[19]姚力波,王仁礼.基于Oracle Spatial空间数据库的GIS数据管理[J].测绘与空间地理信息,2006,29(2):82-86.
[20]魏东,陈晓江,房鼎益.基于SOA体系结构的软件开发方法研究[J].微电子学与计算机,2005,22(6):73-75.
[21]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):13-16.
[22]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003,24-31.
[23]赵文兵,廖湖声,谢昆青.基于GML的地理信息集成系统研究[J].计算机工程与应用,2006,48-48,53.
[24]旷建中,马劲松,蒋民锋.基于GML的多源空间数据集成模型研究[J].计算机应用研究,2005,6:105-107.
[25]李景朝,王永志.我国石油、煤炭、铁矿、钾盐矿产资源潜力数据库建设项目成果报告.中国地质调查局发展研究中心,2008,25-55.
[26]赵鹏大,矿产勘查理论与方法[M].武汉:中国地质大学出版社.2001,88-120.
[27]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003:24-31.
[28]陈丹,袁捷.基于SOA的分布式科研信息系统[J].计算机工程与设计,2006,27(24):4756-4761,4766.
[29]苏炳均,诸昌钤,李林.基于Oracle Spatial的GIS数据组织及查询[J].计算机应用研究,2006,12:278-280.
[30]彭明军,李宗华.基于Oracle Spatial的空间数据互操作[J].计算机工程与应用,2006,32:154-157.
[31]聂独,李晓明,田雪,等.基于Oracle Spatial的配电网GIS数据存储方法[J].2006,26(5):42-45.
[32]王永志,杨毅恒,路来君.基于SOA的油气资源评价系统设计与实现[J].世界地质,2007,26(4):447-452.
[33]王永志,杨毅恒,初娜,赵元艺.德兴铜矿大坞河流域土壤Cu元素形态的聚类分析[J],地球物理学进展,2008,23(1):233-236。
[34]陈苗,杨毅恒,王永志.基于SOA的蒸发岩盆地岩性判别系统[J],地球物理学进展,2008,23(2):528-532.
[35]吴文明,瞿裕忠,董逸生.Web服务及相关技术[J].计算机应用与软件,2004,21(3):14-15.
[36]吴勇.一站式SOA解决方案[R].Oracle Corporation,2007,6-60.
[37]刘冰,卢秀山,田茂义.基于SOA的空间数据管理研究[J].测绘科学,2007,32(1):124-25.
[38]罗英伟,汪小林,许卓群.层次化WebGIS构件系统的设计与实现[J].计算机学报,2004,27(2):177-185.
[39]何牧,孙中轶,蔡鸿明,等.基于网格向SOA提供资源的设计和实现[J].计算机工程,2006,32(21):110-112.
[40]杜攀,徐进.SOA体系下细粒度组件服务整合的探讨[J].计算机应用,2006,26(3):699-702.
[41]桂小林,王庆江,龚文强,等.面向网格计算的机器选择算法研究[J].计算机研究与发展,2004,41(12):2189-2194.
[42]刘湘南,黄方等,GIS空间分析原理与方法[M].北京:科学出版社,2006,138-140.
[43]汤国安、杨昕,ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社,2006,196-233.
[44]李裕伟,赵精满,李晨阳.基于GMS、DSS和GIS的潜在矿产资源评价方法(上下册)[M].北京:地震出版社,2007,101-152.
[45]张发旺,侯新伟,韩占涛.煤炭开发引起水土环境演化及其调控技术[J].地球学报,2001,22(4):345-350.
[46]王文雯,赵彦玲.济南市玉绣河沿岸绿地的生物多样性分析及其优化措施[J].地 球物理学进展,2005,2(2):482-486.
[47]孙扬,王跃思,李昕,等.北京地区一次持续重污染过程O3、NOx、CO的垂直分布分析[J].地球物理学报,2006,49(6):1616-1622
[48]赵元艺,张光第,蔡剑辉.德兴铜矿床地质模型初探[J].西北地质,2003,36增刊:126-129.
[49]郭秀军,武瑞锁,贾永刚,等.不同土壤中含油污水污染区的电性变化研究及污染区探测[J].地球物理学进展,2005,20(2):402-406.
[50]汤懋苍,张拥军,李栋梁.青藏铁路沿线的天灾及其预测方法探索[J].地球物理学报,2006,49(5):1316-1320.
[51]刘熙明,胡非,李磊等.北京地区夏季城市气候趋势和环境效应的分析研究[J].地球物理学报,2006,49(3):689-697.
[52]李婧,刘树华,茅宁豪等.不同生态系统CO2通量和浓度特征分析研究[J].地球物理学报,2006,49(5):1298-1307.
[53]党志,刘丛强,尚爱安.矿区土壤中重金属活动性评估方法的研究进展[J].地球科学进展,2001,16(1):86-92.
[54]李宇庆,陈玲,仇雁翎.上海化学工业区土壤重金属元素形态分析[J].生态环境,2004,13(2):154-155.
[55]何丽莉,白洪涛.用聚类分析方法挖掘Aspect[J].计算机集成制造系统,2006,12(1):149-153.
[56]杨毅恒,韩燕,徐兵,等.多维地学数据处理技术与方法[M].北京:科学出版社,2002,56-79.
[57]刘书暖,张振明,田锡天等.基于聚类分析法的典型工艺路线发现方法[J].计算机集成制造系统,2006,12(7):996-1001.
[58]闫伟,张浩,陆剑峰,等.聚类分析理论研究及在流程企业中的应用[J].计算机工程,2006,32(17):19-21,27.
[59]殷晓曦,许光泉,桂和荣,等.系统聚类逐步判别法对皖北矿区突水水源的分析[J].煤田地质与勘探,2006,32(2):58-61.
[60]李树军,纪宏金.对应聚类分析与变量选择[J].地球物理学进展,2005,20(3):694-697.
[61]闫海涛,胡守云,Appel E,等.土壤剖面中粉煤灰垂向迁移的磁响应[J].地球物理学报,2005,48(6):1392-1399
[62]初娜,赵元艺,张光弟,等.德兴铜矿低品位矿石堆浸场与大坞河流域土壤重金属元素形态的环境特征[J].地质学报,2007,81(5):670-683.
[63]贾承造,魏国齐.“九五”期间塔里木盆地构造研究成果概述[J].石油勘探与开发,2003:30(1):11-14.
[64]李勇,钟建华,温志峰,段宏亮,王海侨.蒸发岩与油气生成、保存的关系[J].沉积学报,2006,24(4):596-606.
[65]张辉,高德利.钻井岩性实时识别方法研究[J].石油钻采工艺,2005,27(1):13-15,80.
[66]刘秀娟,陈超,曾冲,兰磊.利用测井数据进行岩性识别的多元统计方法[J].地质科技情报,2007,26(3):109-112.
[67]原宏壮,陆大卫,张辛耘,等.测井技术新进展综述[J]地球物理学进展,2005,20(3):786-795.
[68]徐海浪,吴小平.电阻率二维神经网络反演[J].地球物理学报,2006,49(2):584-589.
[69]曹正良,王克协,谢荣华,马在田.三种阵列声波测井数据频散分析方法的应用与比较[J].地球物理学报,2005,48(6):1449-1459.
[70]邓小波,聂在平,赵延文,等.用相位感应测井数据反演地层电阻率和介电常数.地球物理学报,2006,49(2):604-608
[71]石油化学工业部化学矿山局,石油勘探中找钾盐矿的方法[M].北京:石油工业出版社,1977.
[72]陈一鸣,朱德怀,任康,等.矿场地球物理测井技术测井资料解释[M].北京:石油工业出版社,1994,120-130.
[73]李新虎.基于不同测井曲线参数集的支持向量机岩性识别对比[J].煤田地质与勘探,2007,35(3):72-76,80.
[74]毛新生.SOA原理、方法、实践[M].电子工业出版社,2007,63-86.
[75]吴庆岩,张爱军译。测井解释常用岩石矿物手册[M].石油工业出版社,1998,10-88。
[76]叶舟,王东.基于规则引擎的数据清洗[J],计算机工程,2006,32(23):52-54.
[77]黎建辉,吴威,阎保平.一种基于XML的元数据映射与转换方法[J],微电子学与计算机,2008,25(1):34-38.
[78]王韦伟,孙庆鸿.基于XML的分布异构数据集成平台[J],2006,36(5):715-719.
[79]叶炜,顾宁.在数据网格环境中可靠获取分布式数据的方法[J].通信学报,2006,27(11):119-124.
[80]唐宇,何凯涛,肖侬,张永波,景宁.国家地质调查应用网格体系及关键技术研究[J].计算机研究与发展,2003,40(12):1682-1683.
[81]赵宏,刘鹏.数据网格建设与系统集成[J].计算机工程与设计,2006,27(23):4424-4426.
[82]王珊,张坤龙.网格环境下的数据库系统[J].计算机应用,2004,24(10):1-3,23.
[83]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务[M].北京:清华大学出版社,2003,70-102.
[84]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):339-341.
[85]邹逸江.面向“数字城市”多源数据的空间数据立方体空间度量研究[J].地理与地理信息科学,2007,23(2):22-25.
[86]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.
[87]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,2007,2199-202.
[98]邹逸江.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.
[89]裴蕾,陶树平.在数据仓库中如何有效地实现数据立方体的计算[J].2005,8:42-45.
(?)徐焱,孙扬.Oracle XSQL技术[M].北京:清华大学出版社,2004,41-53.
(?)赵元艺,王金生.矿床地质环境模型与环境评价[M].北京:地质出版社,2007,(?)7.
(?)来君,矿产资源评价与管理系统研制[],批准登记号2005142,吉林省科技厅。
(?)来君,陆林生,魏远泰.河北地勘局多元地学信息系统研制项目报告[M].1999,
[94]刘成林,塔里木盆地重要蒸发岩坳陷成盐及油气生储条件研究设计书(41005050002),2005-2008,2-10.
[95]李德仁.论空间数据挖掘和知识发现[J].武汉大学学报 信息科学版,2001,26(6):491-499.
[96]李德仁.空间数据挖掘和知识发现理论与方法的研究[J].武汉大学学报 信息科学版,2002,27(3):221-233.
[97]李德仁,关泽群.空间信息系统的集成与实现[M].武汉:武汉大学出版社,2000,92-118.
[98]王树良.基于数据场与云模型的空间数据挖掘和知识发现.武汉:武汉大学,2002,30-42.
[99]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.
[100]于焕菊,谢传节,李云岭,秦承志.中国华北地区地震空间数据仓库的构建与分析[J].地球信息科学,2006,8(3):88-93.
[101]邹逸江.空间数据仓库的概念框架和认知过程[J].计算机应用研究,2007,24(5):186-189.
[102]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.
[103]王紫瑶,南俊杰,段紫辉,等.SOA核心技术及应用[M].北京:电子工业出版社,2008,107-122.
[104]梁爱虎.精通SOA:基于服务总线的Struts+EJB+Web Services整合应用开发.北京:电子工业出版社,2007,227-232.
[105]杨涛,刘锦德.Web Services技术综述一种面向服务的分布式计算模式[J].计算机应用,2004,24(8):1-4.
[106]任捷,吴明晖,应晶.Web Services技术在异构系统集成中的应用研究[J].计算机应用,2004,24(1):95-98.
[107]袁占亭,张秋余,杨洁.基于Web Services的企业应用集成解决方案研究[J].计算机集成制造系统-CIMS,2004,10(4):394-398,414.
[108]王海起,王劲峰.空间数据挖掘技术研究进展[J].地理与地理信息科学,2005,21(4):6-10.
[109]孙庆先,方涛,郭达志.空间数据挖掘中的尺度转换研究[J].计算机工程与应用,2005,16:17-19.
[110]吴亮,谢忠.基于GML的地质图空间数据库交换体系[J].地球科学-中国地质大学学报,2006,31(5):650-652.
[111]兰小机,张书亮,刘德儿,等.GML空间数据库系统研究[J].测绘科学,2005,30(5):16-19.
[112]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务设计.中国地质调查局,2002,4-11.
[113]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务示范报告.中国地质调查局,2005,167-180.
[114]汪民,严光生,叶天竺.基于SIG的资源环境空间信息共享与应用服务研究报告.中国地质调查局,2005,72-136.
[115]叶天竺.全国重要矿产资源潜力预测评价及综合项目总体设计.中国地质调查局,2006,5-12.
[116]叶天竺.全国重要矿产资源潜力预测评价及综合项目2007年总结报告.中国地 质调查局,2007,8-22.
[117]叶天竺.全国重要矿产资源潜力预测评价及综合项目数据库维护设计.中国地质调查局,2007,3-18.
[118]叶天竺.全国重要矿产资源潜力预测评价及综合项目2008年设计.中国地质调查局,2008,2-14.
[119]左群超.全国重要矿产资源潜力预测评价及综合数据模型设计.中国地质调查局,2008,4-10.
[120]杨东来,左群超.国家基础地质数据库整合与集成项目总体设计.中国地质调查局,2008,3-8.
[121]李超岭,吕霞.国土资源信息集成与共享平台建设总体设计.中国地质调查局发展研究中心,2006,4-22.
[122]李超岭,吕霞.国土资源信息集成与共享平台建设2006年度总结.中国地质调查局发展研究中心,2007,10-15.
[123]李超岭,吕霞.国土资源信息集成与共享平台建设项目2007年度总结.中国地质调查局发展研究中心,2007,22-31.
[124]李超岭,吕霞.国土资源信息集成与共享平台建设项目2008年度设计.中国地质调查局发展研究中心,2008,3-18.
[125]郎宝平,姜作勤.地质调查信息化技术支撑体系战略研究报告.中国地质调查局发展研究中心,2007,9-15.
[126]杨东来,姜作勤.国家地质工作信息化发展战略研究报告.中国地质调查局发展研究中心,2007,7-13.
[127]马智民,李霞.主要发达国家地质调查机构信息化跟踪研究报告-英国地调局.中国地质调查局发展研究中心,2007,2-15.
[128]马智民,张伟.主要发达国家地质调查机构信息化跟踪研究报告-澳大利亚地学局.中国地质调查局发展研究中心,2007,21-52.
[129]王群,张伟,王影.主要发达国家地质调查机构信息化跟踪研究报告-加拿大地调局.中国地质调查局发展研究中心,2007,9-16.
[130]马智民,张贷琼.主要发达国家地质调查机构信息化跟踪研究报告-美国地调局.中国地质调查局发展研究中心,2007,30-41.
[131]李景朝,杨东来,姜作勤.中国地质调查局地质数据资源现状与分析.中国地质调查局发展研究中心,2007,2-12.
[132]李超岭、陈辉.地质调查工作主流程信息化研究报告.中国地质调查局发展研究中心,2007,10-22.
[133]国土资源部.国土资源信息核心元数据标准.2003,1-31.
[134]谭永杰.我国石油、煤炭、铁矿、钾盐矿产资源潜力数据库建设设计书.中国地质调查局发展研究中心,2007,4-15.
[135]车长波,景东升.国家级油气资源数据库建设总体设计(初稿).国土资源部油气中心,2007,1-30.
[136]李颖,李伟东.国家级油气资源数据库建设软件设计.吉林大学,2007,2-15.
[137]张道勇.国家级油气资源数据库建设-盆地专题总体设计.国土资源部油气中心,2007,1-9.
[138]王永志.国家级油气资源数据库建设面向对象总体设计.吉林大学,2008,2-40.
[139]Foster I,Kesselman C,Tuecke S.The anatomy of the grid:enabling scalable virtual organization[J].International Journal of High Performance Computing Applications, 2001, 15 (3): 200-222.
[140]Craig Walls, Ryan Breidenbach.Spring in Action.Manning Publications, 2005, 10-33.
[141]John Beatty , Henning Blohm , et al.Service Data Objects For Java Specification.2005, http: //www.bea.com/dev2dev/assets/sdo/SDO_Specification_Java_V2.01.pdf
[142]Michael Beisiegel, Henning Blohm, et al.SCA Service Component Architecture Client and Implementation Model Specification for Java.2005 , http : //dev2dev.bea.com/2005/11/SCA_ClientAndImplementationModelforJava_V09.pdf.
[143]Gonzales M.L. Spatial OLAP: conquering geography.DB2 Magazine, 1999, 4(1): 16-20.
[144]Han J. Towards on-line analytical mining in large databases.ACM SIGMOD-Record, 1998.
[145]Zhou X., Truffer D., Han J. Efficient polygon amalgamation methods for spatial OLAP and spatial data mining.Lecture Notes in Computer Science, 1999, 1651: 167-187。
[146]Atkinson P.M., Lewis, P.Geostatistical classification for remote sensing: an introduction.Computers & Geosciences, 2000, 26: 361-371.
[147]Costa J.P., Pronzato, L., Thierry E.Nonlinear prediction by kriging with application to noise cancellation.Signal Processing, 2000, 80: 553-566.
[148]Wang J., Yang J., Muntz R.An approach to active spatial data mining based on statistical information.IEEE Transactions on Knowledge and Data Engineering, 2000, 12 (5): 715-728.
[149]Wang S.L. A try for handling uncertainties in spatial data mining.Lecture Notes in Artificial Intelligence, 2004, 3215: 513-520.
[150]Wang S.L., Yuan H.N.View-angle of spatial mining.Lecture Notes in Artificial Intelligence, 2006, 4093: 1065-1076.
[151]Tung A.Spatial clustering in the presence of obstacles.IEEE Transactions on Knowledge and Data Engineering, 2001, 359-369.
[152] J.W.Han, N.Stefanovic, K.Koperski.Selective Materialization: An efficient method for spatial data cube construction.PAKDD'98, 1998: 144-158.
[153]Lilian Hobbs, Susan Hillson, Shilpa Lawande, et al.Oracle Database 10g Data Warehousing[M].Oracle Corporation, 2005, Elsevier Digital Press.
[154] Davis Dwight B. Oracle Fusion Architecture Eases the Adoption of Service-Oriented Architecture [R], Oracle Corporation, 2006: 3-15.
[155] Pickersgill Catherine, Whitmore Robin, Bathurst William, et al. Oracle SOA Suite Developer's Guide (10g) [R]. Oracle Corporation, 2006: 1-5.
[156] Brown Alan W., Johnston Simon K., Larsen Grantl. Practice Tutorial of Using IBM Rational Software Platform to Develop SOA [R]. IBM Corporation, 2006: 5-28.
[157] Deanna Bradshaw, Mark Kennedy. Oracle BPEL process manager developer's guide [R]. Oracle Corporation, 2006: 29-33.
[158]William H.I. Building the Data Warehouse [M]. Indianapolis: Wiley Publishing, 2005, 29-33.
[159]Paul L. Oracle Data Warehousing Guide 10g Release 2[OL]. Oracle Corporation, http: //download.oracle.com/docs/cd/B19306_01/server.102/bl4223/toc.htm, 2005.
[160]Marla A. Oracle Content Database Administrator's Guide[OL]. Oracle Corporation, 2006, http: //download.oracle.com/docs/cd/B32119_01/doc/contentdb.1012/b31268.pdf.
[161] Chuck M. Oracle Spatial User's Guide and Reference 10g Release 1 [OL]. Oracle Corporation, http: //download.oracle.com/docs/html/B10826_01/toc.htm, 2003.
[162]Chuck M. Oracle Spatial GeoRaster[OL]. Oracle Corporation, 2005, http://download.oracle.com/docs/pdf/B14254_01.pdf.
[163]Ken C, Orlando C. Oracle SOA Suite Developer's Guide[OL], Oracle Corporation, 2006, http: //download.oracle.com/docs/cd/B31017_01/core.1013/b28764.pdf.
[164]Chuck M. Oracle Application Server MapViewer User's Guide[OL]. Oracle Corporation, 2005, http: //download-west.oracle.com/docs/cd/B14099_19/web.1012/b14036/toc.htm.
[165]Chuck M.Oracle Spatial GeoRaster Developer's Guide 11g Release 1 (11.1) [OL].Oracle Corporation, 2007, 58-122.
[166]Shashaanka Agrawal, Geeta Arora, Eric Belden.Oracle Database Object-Relational Developer's Guide llg Release 1 (11.1) [OL]. Oracle Corporation, 2007, 32-65.
[167]Chuck Murray. Oracle Spatial Developer's Guide 11gRelease 1 (11.1 )[OL].2007.
[168]John P, Dan C, Douglas T. Common Warehouse Metamodel Developer's Guide[M].Indianapolis: Wiley Publishing, 2003, 431-554..
[169]Jiawei Han Jenny Y. Chiang Sonny Chee. DBMiner～～A System for Data Mining in Relational Databases and Data.pdf. http: //db.cs.sfu.ca/
[170]Brian Reistad, Bryan Murray, Doug Davis, et al.Web Services Resource Transfer (WS-RT) .2006, 44-80.
[171]Demed L'Her, Khanderao Kand. What's New in Oracle SOA Suite.Oracle Corporation, 2007, http: //www.oracle.com/technology/products/ias/bpe1/techpreview/s291362-whats-new-in-oracle-soa-suite.pdf.
[172] Deanna Bradshaw, Mark Kennedy.Oracle BPEL Process Manager Developer's Guide 10g (10.1.3.1.0) [OL].Oracle Corporation, 2006, 22-29.
[173] Mark Kennedy.Oracle BPEL Process Manager Quick Start Guide 10g (10.1.3.1.0) [OL].Oracle Corporation, 2006, 43-55.
[174]Oracle. Business Activity Monitoring Datasheet[OL]. Oracle Corporation, 2006, 1-10.
[170]Harpal Kochar .Business Activity Monitoring White Paper[OL]. Oracle Corporation, 2005, 3-8.
[175]Matthew Adams, Xcalia, et al.Service Data Objects For Java Specification[OL]. Oracle Corporation, 2006, 22-32.
[176]Oracle. Oracle Web Services Manager[OL]. Oracle Corporation, 2005, 10-18.
[177]Rick Caccia.An Introduction to Oracle Web Services Manager[OL].Oracle Corporation, 2005, 24-60.
[178]Martin, Chris, Alan, et al.Patterns: Implementing an SOA Using an Enterprise Service Bus[OL].IBM Corporation, 2004, 88-104.
[179]Simon Cox, Paul Daisey. OpenGIS(?) Geography Markup Language (GML) Implementation Specification, 2004, http: //schemas.opengis.net/gml/3.1.1/base/.
[180]K Czajkowski, S Fitzgerald, I Foster et al. Grid information services for dsitributed resource sharing. In: Proc of the 10th IEEE Int'l Symp on High-Performance Distributed Computing (HPDC-10) .San Francisco: IEEE Press, 2001, 1001-1011.
[181 ]Prasad Bagal, Mark Bauer, Eric Belden.Oracle Database Storage Administrator's Guide 11g Release 1 (11.1) [OL].Oracle Corporation, 2008, 101-112.
[182]Mark Bauer, Richard Strohm.Oracle Real Application Clusters Administration and Deployment Guide 11g Release 1 (11.1) [OL]. Oracle Corporation, 2007, 77-89
[183]Ted Husted, Cedric Dumoulin, George Franciscus, David Winterfeldt.Struts in Action[M].Manning, 2003, 86-112.
[184]Lance Ashdown, Janis Greenberg, Jack Melnick.Oracle XML Developer's Kit Programmer's Guide 11g Release 1.Oracle Corporation, 2007, 25-42.
[185]Codd E.Twelve rules for on-line analytic processing. Computer world, 1995.
[186]Inmon W.H. Building the Data Warehouse (Fourth Edition) [M].Wiley, 2007, 80-123.
[187]PAPADIAS D.Efficient OLAP Operation in Spatial Data Warehouses R.HongKong: Technical Report HKUST-CS01-01, 2001, 65-69.
[188]WANGB Y, PAN F.Efficient OLAP Operations for Spatial Data Using Peano Trees C.San Die go: DMKD'03, 2003, 126-130.
[189]Ester M.Spatial data mining: databases primitives, algorithms and efficient DBMS support[J]. Data Ming and Knowledge Discovery, 2000 (4): 193-216。
[190]Kaufman L., Rousseew P.J. Finding Groups in Data: an Introduction to Cluster Analysis[M].New York: John Wiley&Sons, 1990, 58-77.
[191]Koperski K., Adhikary J., Han J.Spatial data mining:process and challenges survey paper.SIGMOD'96 Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'96) .Canada, 1996.
[192]Miller H.J., Han J. Geographic Data Mining and Knowledge Discovery.London: Taylor & Francis, 2001.
[193]Dave Crane, Eric Pascarello, Darren James.Ajax in Action.Manning Publications, 2006, 10-23.
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