基于云计算的土地资源服务高效处理平台关键技术探索与研究
|
摘要
本研究以云计算(Cloud Computing)的关键技术理论为出发点,提出了土地资源信息化管理研究中基于云计算的服务高效处理建模理论框架,解决了空间数据分布式存储策略、空间云服务索引创建与操作、空间数据高效并行操作等关键问题,并通过计算机编程构建了土地资源云平台(Cloud Service Platform of Land Resource,LRCSP)。最后,基于本文的理论框架和建模平台,对土地资源服务的高效处理做了4项实验研究,并展开了深入分析与讨论。
具体来说,文本的研究工作主要包括以下几个方面:
1、总结云计算的基本理论及应用成果。重点讨论了云计算的基本体系结构,关键技术理论和4个成功的商业云计算平台参考架构。从三个学科的视角出发,总结了云GIS的内涵。以上述两点研究为基础提出一种云GIS的六层体系构架:物理层、虚拟层、数据层、服务组件层、服务层和应用层。着重研究了该架构中的云计算各节点的自动部署策略;并针对平台的前台透明服务需求,从GIS服务的特点出发,提出云GIS服务模型,尤其设计了云GIS服务目录和可供用户进行简单编程的服务接口。针对平台的后台处理需求,提出云GIS平台的高性能并行处理数学模型:参照OGC服务链聚合模式,以原子服务和组合服务的角度分析了功能分解性;同时从矢量数据和栅格数据的数据结构出发,分析了两者的数据可分解性。最后,以上述研究为基础,从面向云计算的土地资源服务特点出发,提出面向云计算的土地资源云平台模型(LRCSP)。
2、分析了云计算的关键技术,并以此为基础提出对应的云GIS平台需要解决的关键技术:空间数据的分布式存储策略、虚拟计算节点任务分配模型、基于瓦片的动态地图发布策略以及并行数据库与MapReduce相结合的高效处理模型。在空间数据的分布式存储策略研究中,除了提出基于格网预处理的STRTree的矢量数据并行划分的分布式存储策略和基于四叉树索引的栅格数据的并行划分的分布式存储策略之外,还创新性的提出了基于数据划分的最佳并行策略数学模型。在虚拟计算节点任务分配模型研究中,详细描述了任务分配算法,并在迁移决策阶段提出了计算节点的计算力模型。基于瓦片的动态地图发布策略即解决了一份空间数据经过数据划分并分布存储之后的地图可视化问题,也解决了由于土地数据变更频繁引起的可编辑地图动态更新的问题。在并行数据库与MapReduce相结合的高效处理模型研究中,研究两者优势互补的高效处理模型,并以分地类地物个数统计为例设计了利用MapReduce进行并行统计的算法,为其他类似的并行计算功能提供了借鉴。
3、设计实现了原型平台并进行4组对比测试实验。实现并展示了土地资源云平台的3个功能模块:云资源管理模块、土地业务集成子系统和通用客户端。选取大数据量的矢量和栅格数据对土地资源云平台的4项关键技术进行测试。它们是:云存储性能测试中进行栅格数据并行剖分效率对比测试和矢量数据剖分效果对比测试;地图服务浏览性能测试中通过多次加载多计算节点的海量数据对效率进行测试;高效处理性能测试中对多节点的土地数据进行分类统计,用以确定MapReduce的编程模式下的服务效率;虚拟化负载均衡对比测试中将运行虚拟节点上的LRCSP与只安装一个操作系统的普通PC组成的集群系统进行容错、耗能和运行效率的对比测试。
研究结果表明,本文选取云计算的基本理论、方法和技术解决土地资源管理问题的路径正确;提出的面向云计算的土地资源服务平台模型展现出高效性、灵活性及扩展性,达到预期目标。作者在土地资源云平台的理论研究及实践作为云GIS应用的一个补充,为后续研究与工作提供了良好的基础。
Based on the theory and key technologies of cloud computing, this research firstly proposes a theoretical cloud-computing-based framework for high-performance processing of land resource services. The study also introduces solutions to some key issues such as distributed storage of spatial data, index creation and operation of spatial cloud services and high-performance parallel processing of spatial data. On the basis of the framework, this research establishes a cloud-computing-based application for managing land resource information named Cloud Service Platform of Land Resource (LRCSP). Using this platform, four experiments are performed for testing the efficiency of high-performance processing of land resource cloud services.
Specifically, this research mainly includes three aspects as follows:
1) A cloud-computing-based framework for managing land resources services
This study reviews basic theory and applications of cloud computing, focusing on basic architectures, key technologies and reference architectures of four enterprise cloud computing platforms. From the view of three disciplines, the author discusses definitions of cloud GIS and proposes a six-layer architecture for cloud GIS (physical layer, virtual layer, data source layer, support platform&service component layer and application layer), in which distribution strategy of computing nodes is of great concern. Based on characteristics of GIS services, the author introduces a GIS service model and designs cloud GIS service catalog and service interfaces which allow users to develop some simple programs. The High-performance processing model is a focus of this research. The author analyzes function decomposition in terms of atomic services and composition services, and data decomposition based upon the data structure of vector and raster data. One the basis of the above research, the framework for Cloud Service Platform of Land Resource (LRCSP) is proposed.
2) Key technologies to implement LRCSR
Based on key technologies of cloud computing, this work presents solutions to four main issues for cloud GIS. For distributed storage of spatial data, this research introduces a Grid-aided and STR-Tree-based partition (GASTRSDP) method for dividing vector data and a quadtree-index-based partition approach for partitioning raster data. In particular, this work puts forwards a data-partition-based algorithm for determining the optimal parallel processing strategy. In the research of job scheduling for virtual computing nodes, the author describes job scheduling algorithms in details and proposes a computational model for computing nodes. To solve the problem of map retrieving after the map has been partitioned and parallel stored, this study chooses a tile-caching-based strategy which can also be used to dynamically update land maps once land data are changed. When using parallel database and MapReduce to improve performance, the author designs a Complementary Model. By using the example of counting the instances of each feature’s type of land-use in a layer, the research presents an algorithm for parallel statistics.
3) Implementation of the platform and four experiments to test performance of LRCSP
Three functional modules for client, land resources business and cloud resources management are established. Using a large quantity of vector and raster data, four experiments are conducted on cloud storage performance (parallel partitioning vector and raster data), map display performance (loading mass data of multiple computing nodes), high-performance processing (statistics on land use using MapReduce) and virtual load balancing (comparing the performance and consumptions of virtual nodes to those of Cluster System with common PCs that install only one Operation System). The results show that using cloud theory, methods and technologies to solve the problem of land resource management is promising and that the LRCSP is of high performance, flexibility and scalability, which can shed lights on application of cloud GIS.
具体来说,文本的研究工作主要包括以下几个方面:
1、总结云计算的基本理论及应用成果。重点讨论了云计算的基本体系结构,关键技术理论和4个成功的商业云计算平台参考架构。从三个学科的视角出发,总结了云GIS的内涵。以上述两点研究为基础提出一种云GIS的六层体系构架:物理层、虚拟层、数据层、服务组件层、服务层和应用层。着重研究了该架构中的云计算各节点的自动部署策略;并针对平台的前台透明服务需求,从GIS服务的特点出发,提出云GIS服务模型,尤其设计了云GIS服务目录和可供用户进行简单编程的服务接口。针对平台的后台处理需求,提出云GIS平台的高性能并行处理数学模型:参照OGC服务链聚合模式,以原子服务和组合服务的角度分析了功能分解性;同时从矢量数据和栅格数据的数据结构出发,分析了两者的数据可分解性。最后,以上述研究为基础,从面向云计算的土地资源服务特点出发,提出面向云计算的土地资源云平台模型(LRCSP)。
2、分析了云计算的关键技术,并以此为基础提出对应的云GIS平台需要解决的关键技术:空间数据的分布式存储策略、虚拟计算节点任务分配模型、基于瓦片的动态地图发布策略以及并行数据库与MapReduce相结合的高效处理模型。在空间数据的分布式存储策略研究中,除了提出基于格网预处理的STRTree的矢量数据并行划分的分布式存储策略和基于四叉树索引的栅格数据的并行划分的分布式存储策略之外,还创新性的提出了基于数据划分的最佳并行策略数学模型。在虚拟计算节点任务分配模型研究中,详细描述了任务分配算法,并在迁移决策阶段提出了计算节点的计算力模型。基于瓦片的动态地图发布策略即解决了一份空间数据经过数据划分并分布存储之后的地图可视化问题,也解决了由于土地数据变更频繁引起的可编辑地图动态更新的问题。在并行数据库与MapReduce相结合的高效处理模型研究中,研究两者优势互补的高效处理模型,并以分地类地物个数统计为例设计了利用MapReduce进行并行统计的算法,为其他类似的并行计算功能提供了借鉴。
3、设计实现了原型平台并进行4组对比测试实验。实现并展示了土地资源云平台的3个功能模块:云资源管理模块、土地业务集成子系统和通用客户端。选取大数据量的矢量和栅格数据对土地资源云平台的4项关键技术进行测试。它们是:云存储性能测试中进行栅格数据并行剖分效率对比测试和矢量数据剖分效果对比测试;地图服务浏览性能测试中通过多次加载多计算节点的海量数据对效率进行测试;高效处理性能测试中对多节点的土地数据进行分类统计,用以确定MapReduce的编程模式下的服务效率;虚拟化负载均衡对比测试中将运行虚拟节点上的LRCSP与只安装一个操作系统的普通PC组成的集群系统进行容错、耗能和运行效率的对比测试。
研究结果表明,本文选取云计算的基本理论、方法和技术解决土地资源管理问题的路径正确;提出的面向云计算的土地资源服务平台模型展现出高效性、灵活性及扩展性,达到预期目标。作者在土地资源云平台的理论研究及实践作为云GIS应用的一个补充,为后续研究与工作提供了良好的基础。
Based on the theory and key technologies of cloud computing, this research firstly proposes a theoretical cloud-computing-based framework for high-performance processing of land resource services. The study also introduces solutions to some key issues such as distributed storage of spatial data, index creation and operation of spatial cloud services and high-performance parallel processing of spatial data. On the basis of the framework, this research establishes a cloud-computing-based application for managing land resource information named Cloud Service Platform of Land Resource (LRCSP). Using this platform, four experiments are performed for testing the efficiency of high-performance processing of land resource cloud services.
Specifically, this research mainly includes three aspects as follows:
1) A cloud-computing-based framework for managing land resources services
This study reviews basic theory and applications of cloud computing, focusing on basic architectures, key technologies and reference architectures of four enterprise cloud computing platforms. From the view of three disciplines, the author discusses definitions of cloud GIS and proposes a six-layer architecture for cloud GIS (physical layer, virtual layer, data source layer, support platform&service component layer and application layer), in which distribution strategy of computing nodes is of great concern. Based on characteristics of GIS services, the author introduces a GIS service model and designs cloud GIS service catalog and service interfaces which allow users to develop some simple programs. The High-performance processing model is a focus of this research. The author analyzes function decomposition in terms of atomic services and composition services, and data decomposition based upon the data structure of vector and raster data. One the basis of the above research, the framework for Cloud Service Platform of Land Resource (LRCSP) is proposed.
2) Key technologies to implement LRCSR
Based on key technologies of cloud computing, this work presents solutions to four main issues for cloud GIS. For distributed storage of spatial data, this research introduces a Grid-aided and STR-Tree-based partition (GASTRSDP) method for dividing vector data and a quadtree-index-based partition approach for partitioning raster data. In particular, this work puts forwards a data-partition-based algorithm for determining the optimal parallel processing strategy. In the research of job scheduling for virtual computing nodes, the author describes job scheduling algorithms in details and proposes a computational model for computing nodes. To solve the problem of map retrieving after the map has been partitioned and parallel stored, this study chooses a tile-caching-based strategy which can also be used to dynamically update land maps once land data are changed. When using parallel database and MapReduce to improve performance, the author designs a Complementary Model. By using the example of counting the instances of each feature’s type of land-use in a layer, the research presents an algorithm for parallel statistics.
3) Implementation of the platform and four experiments to test performance of LRCSP
Three functional modules for client, land resources business and cloud resources management are established. Using a large quantity of vector and raster data, four experiments are conducted on cloud storage performance (parallel partitioning vector and raster data), map display performance (loading mass data of multiple computing nodes), high-performance processing (statistics on land use using MapReduce) and virtual load balancing (comparing the performance and consumptions of virtual nodes to those of Cluster System with common PCs that install only one Operation System). The results show that using cloud theory, methods and technologies to solve the problem of land resource management is promising and that the LRCSP is of high performance, flexibility and scalability, which can shed lights on application of cloud GIS.
引文
Abadi D J.Data management in the cloud: Limitations and opportunities[J].Data Engineering,2009:3.
Aboulnaga A,Salem K,Soror A A et al.Deploying database appliances in the cloud[J].Data Engineering,2009,32(1):13.
Abugov D.Oracle Spatial Partitioning: Best Practices (an Oracle White Paper)[M]:Oracle Inc.,2004.
Achalakul T,Taylor S.A distributed spectral-screening PCT algorithm[J].Journal of Parallel and Distributed Computing,2003,63(3):373-384.
Amazon.Amazon elastic compute cloud.http://aws.amazon.com/ec2/[EB/OL].2010. Anderson T E,Culler D E,Patterson D A.A case for NOW (Networks of Workstations)[J].IEEE micro,1995,15(1):54-64.
Apache.Welcome to Apache Hadoop!.http://hadoop.apache.org/[EB/OL].2010.
Armbrust M , Fox A , Griffith R et al . Above the clouds: A berkeley view of cloud computing[J] . EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28,2009.
Attiya H,Welch J.Distributed computing: fundamentals, simulations, and advanced topics[M]:Wiley-Interscience,2004.
Baker M.Cluster computing white paper[M],2000.
Baker M,Fox G,Yau H.Cluster computing review[M],1995.
Balasubramaniam M,Barker K,Banicescu I et al.A novel dynamic load balancing library for cluster computing[C]:IEEE,2004:346-353.
Baranski B,Sch Ffer B,Redweik R.Geoprocessing in the Clouds[C].Free and Open Source Software for Geospatial Conference.Sydney, Australia,2009.
Barham P,Dragovic B,Fraser K et al.Xen and the art of virtualization[C].Proceedings of the nineteenth ACM symposium on Operating systems principles:ACM,2003:164-177.
Batcher K E.Design of a massively parallel processor[J].IEEE Transactions on Computers,1980:836-840.
Berman F,Fox G,Hey A.Grid computing: making the global infrastructure a reality[M]:John Wiley & Sons Inc,2003.
Bertino E,Paci F,Ferrini R et al.Privacy-preserving digital identity management for cloud computing[J].Data Engineering,2009:21.
Bertogna M,Cirinei M.Response-time analysis for globally scheduled symmetric multiprocessor platforms[C].28th IEEE Real-Time Systems Symposium (RTSS).
Bertogna M,Cirinei M.Response-Time Analysis for Globally Scheduled Symmetric Multiprocessor Platforms[C].28th IEEE International Real-Time Systems Symposium, 2007. RTSS 2007:IEEE Computer Society Washington, DC, USA,2007:149-160.
Beyer K,Ercegovac V,Krishnamurthy R et al.Towards a scalable enterprise content analytics platform[J].IEEE Data Eng. Bull,2009,32(1):28-35.
Blower J D . GIS in the cloud: implementing a Web Map Service on Google App Engine[C].Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application:ACM,2010:1-4.
Boss G,Malladi P,Quan D et al.Cloud computing. IBM White Paper.http://download.boulder.ibm .com/ibmdl/pub/software/dw/wes/hipods/Cloud_computing_wp_final_8Oct.pdf[EB/OL]. Boulon J,Konwinski A,Qi R et al.Chukwa, a large-scale monitoring system[J].Cloud Computing and its Applications,2008:1-5.
Brauner J,Foerster T,Schaeffer B et al.Towards a Research Agenda for Geoprocessing Services[C].12th AGILE International Conference on Geographic Information Science,2009.
Brinkhoff T,Kriegel H P,Seeger B.Efficient processing of spatial joins using R-trees[J].ACM SIGMOD Record,1993,22(2):237-246.
Bryant R E.Data-intensive supercomputing: The case for DISC[J].School of Computer Science, Carnegie Mellon University, Tech. Rep. Technical Report CMU-CS-07-128,2007.
Burrows M.The Chubby lock service for loosely-coupled distributed systems[C]:USENIX Association,2006:350.
Buyya R.High Performance Cluster Computing: Architectures and Systems, Volume 1[J].Prentice Hall PTR,1999,82:327-350.
Buyya R,Yeo C S,Venugopal S.Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities[C].10th IEEE International Conference on High Performance Computing and Communications, 2008. HPCC'08,2008:5-13.
Capra L,Mascolo C,Zachariadis S et al.Towards a mobile computing middleware: a synergy of reflection and mobile code techniques[C]:Published by the IEEE Computer Society,2001:148.
Carr N.The Big Switch: Rewiring the World, From Edison to Google[M].London:W. W. Norton & Company,2008.
Chalermwat P.High performance automatic image registration for remote sensing[D].Doctor,Citeseer,1999.
Chang F,Dean J,Ghemawat S et al.Bigtable: A distributed storage system for structured
data[J].ACM Transactions on Computer Systems (TOCS),2008,26(2):4.
Charles J.Middleware moves to the forefront[J].Computer,1999,32(5):17-19.
Chau S C,Fu A W C.Load balancing between computing clusters[C]:IEEE,2003:548-551.
Chen Q,Wang L,Shang Z.MRGIS: A MapReduce-Enabled High Performance Workflow System for GIS[C].IEEE Fourth International Conference on eScience, 2008. eScience'08,2008:646-651.
Chien A,Calder B,Elbert S et al.Entropia: architecture and performance of an enterprise desktop grid system[J].Journal of Parallel and Distributed Computing,2003,63(5):597-610.
Cook S,Dwork C,Reischuk R.Upper and lower time bounds for parallel random access machines without simultaneous writes[J].SIAM Journal on Computing,1986,15:87.
Crockett T W.An introduction to parallel rendering[J].Parallel Computing,1997,23(7):819-843.
Crookes D.Architectures for high performance image processing[J].The future Journal of Systems Architecture,1999,45(10):739-748.
Dean J,Ghemawat S.Distributed programming with Mapreduce[J].Beautiful Code. Sebastopol: OReilly Media, Inc,2007:371-384.
Dean J,Ghemawat S.MapReduce: Simplified data processing on large clusters[J].Communications of the ACM,2008,51(1):107-113.
Dean J,Ghemawat S.Mapreduce: a flexible data processing tool[J].Communications of the ACM,2010,53(1):72-77.
DeWitt D J,Stonebraker M.MapReduce: A major step backwards[J].The Database Column,2008,1.
Dhodhi M K,Saghri J A,Ahmad I et al.D-ISODATA: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations[J].Journal of Parallel and Distributed Computing,1999,59(2):280-301.
Django.The Django Framework.http://www.djangoproject.com/[EB/OL].2010-6-10.
Enslow P H.What is" Distributed" Data Processing System?[J].Computer ,1978,11(1):13-21.
Fang L,Huang X,Pan N et al.A New Dynamic Tile Caching Method for WebGIS[C].the International Conference onMultimedia Technology (ICMT):IEEE,2010:1-4.
Fenn J,Raskino M,Gammage B .Gartner's Hype Cycle Special Report for 2009[R] ,http://www.gartner.com/DisplayDocument?ref=g_search&id=1108412&subref=simplesearch,2009.
Fleury M,Self R P,Downton A C.Development of a fine-grained parallel Karhunen-Loève transform[J].Journal of Parallel and Distributed Computing,2004,64(4):520-535.
Fortune S,Wyllie J.Parallelism in random access machines[C]:ACM,1978:114-118.
Foster I.What is The Grid? A Three Point Checklist[J].GRIDToday,2002b,01(06):22-25.
Foster I,Kesselman C.The grid: blueprint for a new computing infrastructure[M].San Fransisco, CA, USA:Morgan Kaufmann Publishers Inc.,2004.
Foster I,Kesselman C,Nick J et al.The physiology of the grid: An open grid services architecture for distributed systems integration[C]:Edinburgh,2002a:1-5.
Foster I,Kesselman C,Tuecke S.The Anatomy of the Grid: Enabling Scalable Virtual Organizations[J].International Journal of High Performance Computing Applications,2001,15(3):200-222.
Foster I,Zhao Y,Raicu I et al.Cloud computing and grid computing 360-degree compared[C].Grid Computing Environments Workshop, 2008. GCE'08,2008:1-10.
Fu X,Wang D,Zheng W et al.GPR-Tree: a global parallel index structure for multiattribute declustering on cluster of workstations[C].Advances in Parallel and Distributed Computing:IEEE,2002:300-306.
Ganapathi A,Kuno H,Dayal U et al.Predicting multiple metrics for queries: Better decisions enabled by machine learning[C]:Citeseer,2009.
Ghemawat S,Dean J.MapReduce: Simplified Data Processing on Large Clusters[J].Usenix SDI,2004:137-150.
Ghemawat S,Gobioff H,Leung S T.The Google file system[J].ACM SIGOPS Operating Systems Review,2003,37(5):43.
Gillett F E,Brown G E,Staten J et al.The new tech ecosystems of cloud, cloud services, and cloud computing[R],Forrester Research Report,2008.
Goldberg R P.Survey of virtual machine research[J].IEEE Computer,1974,7(6):34-45.
Google.Google App Engine.http://appengine.google.com/[EB/OL].2010.
Google.Google Maps JavaScript API V3.http://code.google.com/intl/zh-CN/apis/maps/documen tation/javascript/[EB/OL].2010.
Greggo A.Cloud computing in the Enterprise: An Overview[C].Enterprise Computing Conference,2009.
Guo J , Bhuyan L N . Load balancing in a cluster-based web server for multimedia applications[J].IEEE Transactions on Parallel and Distributed Systems,2006,17(11):1321-1334.
Hastorun D,Jampani M,Kakulapati G et al.Dynamo: amazon s highly available key-value store[C]:Citeseer,2007:205-220.
Henry A.Using Google Earth for Internet GIS[J],2009.
Hewitt C.ORGs for scalable, robust, privacy-friendly client Cloud Computing[J].IEEE internet computing,2008,12(5):96-99.
Hewlett-Packard.HP Integrated Lights-Out 2 User Guide[R],2009.
Hibler M , Ricci R , Stoller L et al . Large-scale virtualization in the emulab network testbed[C].USENIX 2008 Annual Technical Conference on Annual Technical Conference:USENIX Association,2008:113-128.
Houston I,King S.CICS project report experiences and results from the use of Z in IBM[C]:Springer,1991:588-596.
Hwang K . Advanced computer architecture: parallelism, scalability, programmability[M] :McGraw-Hill New York,1993.
Ibrahim M A M,Xinda L.Performance of dynamic load balancing algorithm on cluster of workstations and PCs[C]:IEEE,2003b:44-47.
Ibrahim M,Xinda L.Utilization of cluster of PCs for the study of dynamic load balancing[C]:IEEE,2003a:335-337.
Isard M,Budiu M,Yu Y et al.Dryad: distributed data-parallel programs from sequential building blocks[J].ACM SIGOPS Operating Systems Review,2007,41(3):72.
ISO.Geographic information-Services[S].http://portal.opengeospatial.org/files/?artifact_id=1221,2001-09-14.
Ji-hong G,Shui-geng Z,Fu-ling B et al.Building Distributed Web GIS: A Mobile-Agent Based Approach[J].Wuhan University Journal of Natural Sciences,武汉大学学报(自然科学版.英文版),2001,6(2):474-481.
Joyent.Joyent Smart Computing.http://www.joyent.com/[EB/OL].2010-7-10.
Kalla R,Sinharoy B,Tendler J M.IBM Power5 chip: A dual-core multithreaded processor[J].IEEE micro,2004,24(2):40-47.
Kamel I,Faloutsos C.Hilbert R-tree: An improved R-tree using fractals[C].the 20th VLDB Conference.Santago,Chile:Citeseer,1994.
Keahey K,Freeman T.Contextualization: Providing one-click virtual clusters[C].Fourth IEEE International Conference on eScience:IEEE,2008:301-308.
Kepner J.A Multi-Threaded Fast Convolver for Dynamically Parallel Image Filtering[J].Journal of Parallel and Distributed Computing,2001,63(3):360-372.
Kepp A.Caching Dynamic Servers.http://opengeo.org/products/coredevelopment/geowebcache/tile expiration/[EB/OL].2009.
Kivity A,Kamay Y,Laor D et al.KVM: the Linux virtual machine monitor[C],2007:225-230.
Kumar V.Introduction to parallel computing[M].Boston, MA, USA:Addison-Wesley Longman Publishing Co., Inc. ,2002.
Lai K,Rasmusson L,Adar E et al.Tycoon: An implementation of a distributed, market-based resource allocation system[J].Multiagent and Grid Systems,2005,1(3):169-182.
Lee O,Anshel M,Chung I.Design of an efficient load balancing algorithm on distributed networks by employing symmetric balanced incomplete block design[C]:IEE Proc. Commun,2004:535-538.
Lei F,Shen-jun Y,Ting L et al.A Cloud Computing Application in Land Resources Information Management[C]:IEEE,2010:388-393.
Lenk A,Klems M,Nimis J et al.What's inside the Cloud? An architectural map of the Cloud landscape[C]:IEEE Computer Society,2009:23-31.
Lenoski D,Laudon J,Gharachorloo K et al.The stanford dash multiprocessor[J].Computer,1992,25(3):79.
Leutenegger S T,Lopez M A,Edgington J.STR: A simple and efficient algorithm for R-tree packing[C].Proceedings. 13th International Conference on Data Engineering:IEEE,2002:497-506.
Litty L,Lagar-Cavilla H A,Lie D.Computer Meteorology: Monitoring Compute Clouds[C],2009.
Liu H,Orban D.Gridbatch: Cloud computing for large-scale data-intensive batch applications[C],2008:295-305.
LVSKB.Load balancing.http://kb.linuxvirtualserver.org/wiki/Load_balancing#Computing_Load_ Balancing[EB/OL].2008.
McKeown N,Anderson T,Balakrishnan H et al.OpenFlow: enabling innovation in campus networks[J].ACM SIGCOMM Computer Communication Review,2008,38(2):69-74.
Microsoft.Windows Azure Platform.http://www.microsoft.com/windowsazure/[EB/OL].2010-7 -10.
Milojicic D S,Kalogeraki V,Lukose R et al.Peer-to-peer computing[M]:Citeseer,2002.
Milojicic D S,LaForge W,Chauhan D.Mobile objects and agents (MOA)[J].Distributed Systems Engineering,1998,5:214.
Moretti C,Bulosan J,Thain D et al.All-pairs: An abstraction for data-intensive cloud computing[C]:Citeseer,2008.
Nativi S,Ramamurthy M,Woolf A.Towards Earth and Space Science digital infrastructures: network, computing and data services[J].editorial of the International Journal of Digital Earth,2009,2(Supplement 1):1-2.
Newman A,Li Y F,Hunter J.Scalable Semantics–the Silver Lining of Cloud Computing[C],2008:111-118.
Nurmi D,Wolski R,Grzegorczyk C et al.Eucalyptus: A technical report on an elastic utility computing archietcture linking your programs to useful systems[R],Computer Science Tech.rep,2008.
Nurmi D,Wolski R,Grzegorczyk C et al.The eucalyptus open-source cloud-computing system[C]:IEEE Computer Society,2009:124-131.
Nyland L,Prins J,Goldberg A et al.A refinement methodology for developing data-parallel applications[C]:Springer,1996:145-150.
Oberheide J,Cooke E,Jahanian F.Cloudav: N-version antivirus in the network cloud[C]:USENIX Association,2008:91-106.
OpenIDFoundation.OpenID[J].Retrieved November,2008a,24:2008.
OpenIDFoundation.OpenID homepage.http://openid.net/[EB/OL].2010-7-10.
Pavlo A,Paulson E,Rasin A et al.A comparison of approaches to large-scale data analysis[C]:ACM,2009:165-178.
Pearson S.Taking account of privacy when designing cloud computing services[C]:IEEE Computer Society,2009:44-52.
Pfister G F.In search of clusters: the coming battle in lowly parallel computing[M].Upper Saddle River, NJ, USA:Prentice-Hall, Inc. ,1995.
Plaza A J.Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations[J].Journal of Parallel and Distributed Computing,2008,68(1):93-111.
P?idal M P,?abi?ka P.Tiles as an approach to on-line publishing of scanned old maps, vedute and other historical documents[J].e-Perimetron,2008,3(1):10-21.
Quinn M J.Parallel computing: theory and practice[M]:McGraw-Hill, Inc. New York, NY, USA,1994.
Ranka S,Won Y,Sahni S.Programming a hypercube multicomputer[J].Software, IEEE,2002,5(5):69-77.
Reid-Miller M.List ranking and list scan on the Cray C-90[C]:ACM,1994:104-113. Ritter G X,Gader P D.Image algebra techniques for parallel image processing* 1[J].Journal of Parallel and Distributed Computing,1987,4(1):7-44.
Satoh I.MobileSpaces: A framework for building adaptive distributed applications using a hierarchical mobile agent system[C]:Published by the IEEE Computer Society,2000:161.
Schnitzer B,Leutenegger S T.Master-Client R-trees: A New Parallel R-tree Architecture[C].the 11th International Conference on Scientific and Statistical Database Management:IEEE Computer Society,1999:68.
Schwiegelshohn U,Badia R M,Bubak M et al.Perspectives on grid computing[J].Future Generation Computer Systems,2010,26(8):1104-1115.
Shekhar Shashi,Chawla Sanjay.空间数据库[M].北京:机械工业出版社,2004.
Silberschatz A.,Korth H. F.,Sudarshan S..数据库系统概念[M].北京:机械工业出版社,2000.
Sims K.IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing.http://www-03.ibm.com/press/us/en/pressrelease/22613.wss[EB/OL].
Sit H Y,Ho K S,Leong H V et al.An adaptive clustering approach to dynamic load balancing[C]:IEEE,2004:415-420.
Sotomayor B,Montero R,Llorente I M et al.Capacity leasing in cloud systems using the opennebula engine[J].Cloud Computing and Applications,2008,2008.
Srikantaiah S,Kansal A,Zhao F.Energy aware consolidation for cloud computing[C],2008.
Stonebraker M,Abadi D,DeWitt D J et al.MapReduce and parallel DBMSs: friends or foes?[J].Communications of the ACM,2010,53(1):64-71.
Stra Er M,Baumann J,Hohl F.Mole-a java based mobile agent system[C],1996.
Subhlok J,Vondran G.Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations[J].Journal of Parallel and Distributed Computing,2000,60(3):297-319.
Sumanth J V,Swanson D R,Jiang H.Adaptive Load Balancing for Long-Range MD Simulations in A Distributed Environment[C].Proceedings of International Conference on Parallel Processing,2006:135-146.
SunMicrosystems.Project Caroline.http://labs.oracle.com/projects/caroline/[EB/OL].2010-6-20. Sun公司.云计算入门指南.http://cn.sun.com/offers/docs/sun_cloudcomputing_chinese.pdf.[EB/OL ],2009.
Tuszy Nacute J.Parallelizing the construction of indirect access arrays for shared-memory machines[J].Communications in Numerical Methods in Engineering,1998,14(8):773-781.
Vaquero L M,Rodero-Merino L,Caceres J et al.A break in the clouds: towards a cloud definition[J].ACM SIGCOMM Computer Communication Review,2008,39(1):50-55.
VMware.VMware Infrastructure.http://www.vmware.com.cn/pproducts/vi[EB/OL]. WANG B,HORINOKUCHI H,KANEKO K et al.Implementation and Evaluation of Parallel R-tree on Parallel Object Database System ShusseUo.[J].Transactions of Information Processing Society of Japan,1999,40:92-103.
Wang B,Horinokuchi H,Kaneko K et al.Parallel R-tree search algorithm on DSVM[C].the 6th International Conference on Database Systems for Advanced Applications:IEEE,2002:237-244.
Wang L,Tao J,Kunze M et al.The Cumulus Project: Build a scientific cloud for a data center[J].Cloud Computing and its Applications,2008a.
Wang L,Tao J,Kunze M et al.Scientific cloud computing: Early definition and experience[C].10th IEEE International Conference on High Performance Computing and Communications, 2008. HPCC'08,2008b:825-830.
Wang Y,Wang S,Zhou D.Retrieving and Indexing Spatial Data in the Cloud Computing Environment[J].Cloud Computing,2009:322-331.
Weiss A.Computing in the clouds[J].NetWorker,2007,11(4):16-25.
Wooldridge M,Jennings N.Agent theories, architectures, and languages: a survey[J].Intelligent agents,1995:1-39.
Writer E.Tiles on a Cloud-Cloud computing and ArcGIS Server deliver a thrifty solution[J],2010. Wu M Y,Shu W.A load-balancing algorithm for n-cubes[C]:IEEE,2002:148-155.
Wu S,Wu K L.An indexing framework for efficient retrieval on the cloud[J].Data Engineering,2009:75.
XenSource.Xen.http://cn.opensuse.org/Xen_Virtual_Machine_Overview[EB/OL]. Xia Y,Chen S,Korgaonkar V.Load balancing with multiple hash functions in peer-to-peer networks[C].Proceedings of the 12th International Conference on Parallel and Distributed Systems-Volume 1:IEEE Computer Society,2006:411-420.
Xian F,Srisa-an W,Jiang H.Contention-aware scheduler: unlocking execution parallelism in multithreaded java programs[C].ACM SIGPLAN Notices 10:ACM,2008:163-180.
Xiao Y,Guizani M.Optimal paging load balance with total delay constraint in macrocell-microcell hierarchical cellular networks[J].Wireless Communications, IEEE Transactions on,2006,5(8):2202-2209.
Yan W,Wu E.Toward Automatic Discovery of Malware Signature for Anti-virus Cloud Computing[J].Complex Sciences,2009:724-728.
Yeo C S,De Assuncao M D,Yu J et al.Utility Computing and Global Grids[J].Arxiv preprint cs/0605056,2006.
Youseff L,Butrico M,Da Silva D.Toward a unified ontology of cloud computing[C].Grid Computing Environments Workshop, 2008. GCE'08,2008:1-10.
Yu Y,Gunda P K,Isard M.Distributed aggregation for data-parallel computing: interfaces and implementations[C]:ACM,2009:247-260.
Yu Y,Isard M,Fetterly D et al.DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language[C].Symposium on Operating System Design and Implementation(OSDI).San Diego,2008.
Zhang Q,Riska A,Sun W et al.Workload-aware load balancing for clustered web servers[J].IEEE Transactions on Parallel and Distributed Systems,2005,16(3):219-233.
Ziavras S G,Meer P.Adaptive multiresolution structures for image processing on parallel computers[J].Journal of Parallel and Distributed Computing,1994,23(3):475-483.
百度百科.云计算.http://baike.baidu.com/view/1316082.htm[EB/OL],2009a.
百度百科.云安全.http://baike.baidu.com/view/1725454.htm?fr=ala0_1_1[EB/OL],2009b.
百度百科.计算机集群.http://baike.baidu.com/view/302477.htm[EB/OL],2010.
蔡孟裔.新编地图学教程[M]:高等教育出版社,2000.
陈国良.并行算法实践[M].北京:高等教育出版社,2004.
陈国良.并行算法研究进展[J].中国计算机学会通讯,2005,1(2):18-21.
陈国良,吴俊敏,章锋.并行计算机体系结构[M].北京:高等教育出版社,2002.
陈海波.云计算平台可信性增强技术的研究[D].博士学位论文,复旦大学,2008.
陈康.云计算后台大规模数据处理技术探讨[J].电信工程技术与标准化,2009b(011):12-16.
陈康,郑纬民.云计算:系统实例与研究现状[J].Journal of Software,2009a,20(5):1337-1348.
陈能成,龚健雅,朱欣焰等.基于J2EE的分布式GIS研究[J].测绘学报,2003,32(002):158-163.
陈全,邓倩妮.云计算及其关键技术[J].计算机应用,2009,29(9):2562-2567.
陈育春.Google Maps API开发大全[M].北京:机械工业出版社,2010.
陈志荣,李昭,刘婷.面向移动用户的空间信息网格服务模型[J].浙江大学学报:理学版,2010,37(005):577-582.
戴立乾,陈娜.浅议云计算时代下GIS的发展[J].安徽农业科学,2009,37(031):15556-15557.
戴元顺.云计算技术简述[J].信息通信技术,2010,4(002):29-35.
邓自立.云计算中的网络拓扑设计和Hadoop平台研究[D].硕士学位论文,中国科学技术大学,2009.
董开坤,胡铭曾,方滨兴等.一个基于图像代数的并行图像处理环境[J].计算机研究与发展,2004,41(001):201-206.
方金云,何建邦.网格GIS体系结构及其实现技术[J].地球信息科学,2002,4(004):36-42.
方金云,何建邦.并行栅格数据处理网格服务节点软件的关键技术[J].地球信息科学,2004,6(001):17-21.
付志超.基于Map/Reduce的分布式智能搜索引擎框架研究[D].硕士学位论文,武汉理工大学,2008.
龚健雅.地理信息系统基础[M].北京:科学出版社,2001.
龚雪晶,慈林林,姚康泽.基于邻域信息的遥感图像模糊聚类及并行算法设计[J].计算机应用,2007,27(010):2512-2514.
桂小林.网格技术导论[M].北京:北京邮电大学出版社,2004:4-6.
国土资源部.城镇地籍数据库标准(行业试行)[S],2001.
国土资源部.土地基本术语[S],2003.
国土资源部.城镇地籍数据库标准[S],2007.
国务院.国务院关于开展第二次全国土地调查的通知国发[2006]38号.http://www.gov.cn/zwgk/2006-12/20/content_474183.htm[EB/OL],2006.
国务院第二次全国土地调查领导小组办公室.第二次全国土地调查总体方案[S],2007-6-5.
国务院第二次全国土地调查领导小组办公室.第二次全国土地调查数据库更新技术规范(试行)[S],2009.
韩琼.土地管理信息化方案与策略研究[D].博士学位论文,中国地质大学,2003.
韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(006):91-94.
何建农.网格计算技术在土地信息管理中的应用[C].Web技术与应用.全国第五次程序设计
语言发展与教学学术会议、第三届全国Web信息系统及其应用学术会议暨全国首届语义Web与本体论学术研讨会.南京,2006:219-222.
何亚文,杜云艳,苏奋振等.利用空间信息网格的海流场远程可视化[J].武汉大学学报:信息科学版,2010,35(003):350-352.
胡玥,高庆狮,高小宇.串行算法并行化基础[M].北京:科学出版社,2008.
黄德霖,鲍家伟.浅谈土地信息系统标准化问题[J].浙江国土资源,2005(06):47-49.
黄国满,郭建峰.分布式并行遥感图像处理中的数据划分[J].遥感信息,2001,2:9-12.
黄舟.网格环境下空间计算任务处理技术研究[D].博士学位论文,北京大学,2009.
纪俊.一种基于云计算的数据挖掘平台架构设计与实现[D].硕士学位论文,青岛大学,2009.
金江军,潘懋.网格技术及其在国土资源信息化中的应用探讨[J].国土资源信息化,2005(01):
金哲凡,杨建,石教英.分布式并行绘制系统中几何指令流压缩的研究与实现[J].计算机辅助设计与图形学学报,2002,14(009):824-828.
靳华中,孟令奎,王显.集群环境下并行GIS的体系结构设计[J].地理空间信息,2005,3(5).
景晓军,蔡安妮.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(004):71-76.
李德仁,宾洪超.国土资源网格管理平台的框架设计与实现[J].测绘科学,2008a,33(01):8-9.
李德仁,宾洪超,邵振峰.国土资源网格化管理与服务系统的设计与实现[J].武汉大学学报(信息科学版),2008b,33(01):1-6.
李德仁,朱欣焰,龚健雅.从数字地图到空间信息网格——空间信息多级网格理论思考[J].武汉大学学报(信息科学版),2003,28(06):642-650.
李国杰.信息服务网络——第三代Internet[R].亚太经合组织计交会科技论坛报告集.中国,2001:150-152.
李松伟.盛大AMD云计算联合实验室落户上海.http://tech.qq.com/a/20100419/000271.htm[EB/ OL],2010.
李霞,张基宏.基于遗传算法的任务分配问题求解[J].数据采集与处理,1999,14(003):293-297.
李夏英.浙江省土地管理体制改革若干问题的研究[D].硕士学位论文,华东师范大学,2006.
刘凡茂.基于云计算的乡镇卫生院信息化研究[D].硕士学位论文,中南大学,2009.
刘鹏.网格概念的界定.http://www.chinagrid.net/grid/paperppt/GridConcept.pdf[EB/OL],2003. 刘鹏.云计算[M].北京:电子工业出版社,2010.
刘鹏程.云计算中虚拟机动态迁移的研究[D].硕士学位论文,复旦大学,2009.
刘晓沐,岳丽华,陈博等.遥感图像目标识别的并行处理方法[J].计算机应用,2007,27(009):2123-2125.
刘旭辉,韩冀中,贺劲等.基于集群系统的空间数据并行处理策略研究[J].高技术通讯,2009,19(010):991-997. 刘异,呙维,江万寿等.一种基于云计算模型的遥感处理服务模式研究与实现[J].计算机应用研究,2009(009):3428-3431.
刘勇,朱国强.基于TCP/IP的网络并行计算的实现[J].计算机工程,2000,26(008):187-189.
刘志军,丁明跃,周成平等.基于并行遗传算法的图像超分辨率复原[J].中国图象图形学报: A 辑,2004,9(001):62-68.
陆松,刘光明.分布共享存储的遥感图像并行预处理系统结构研究[J].计算机工程与科学,2004,26(010):56-59.
骆剑承,周成虎,蔡少华等.基于中间件技术的网格GIS体系结构[J].地球信息科学,2002,4(003):17-25.
吕捷,张天序,张必银.MPI并行计算在图像处理方面的应用[J].红外与激光工程,2004,33(005):496-499.
马荣华,黄杏元.大型GIS海量数据分布式组织与管理[J].南京大学学报:自然科学版,2003,39(006):836-843.
马维峰,王晓蕊,高松峰等.基于服务器动态缓存和Ajax技术的WebGIS开发[J].测绘科学,2008,33(005):204-205.
钱巍,吕晶,李晗静.一种机群系统下的并行图像处理环境[J].哈尔滨师范大学自然科学学报,2005,21(002):61-65.
沈占锋,骆剑承,陈秋晓等.基于MPI的遥感影像高效能并行处理方法研究[J].中国图象图形学报,2007,12(012):2132-2136.
宋关福.GIS软件:从纸空间到真空间的蜕变.http://www.supermap.com.cn/sup/xwtxpage. asp?orderID=140[EB/OL],2009.
苏日娜,王宇.基于免疫遗传算法的负载均衡策略[J].计算机应用,2010(010):2595-2597.
孙宏元.基于HPC的多分辨空间信息应用基础平台关键技术研究[D].博士学位论文,西安电子科技大学,2006.
陶卫中.MODIS数据预处理的并行算法设计[D].硕士学位论文,华中科技大学,2007.
滕龙妹.土地资源时空数据网格服务模型及其实现方法[D].博士学位论文,浙江大学;,2008.
王鄂,李铭.云计算下的海量数据挖掘研究[J].现代计算机:下半月版,2009(011):22-25.
王家耀,孙庆辉,吴明光等.面向智能空间信息服务的网格GIS节点构建[J].武汉大学学报: 信息科学版,2009,34(001):1-6.
王茜蒨,彭中,刘莉.一种基于自适应阈值的图像分割算法[J].北京理工大学学报,2003,23(004):521-524.
王升平,李青.数据复制管理中共享缓存数据流的实现[J].计算机工程,2007,33(20):83.
王怡,周明全,耿国华.基于简化Mumford-Shah模型的水平集图像分割算法[J].计算机应用,2006,26(008):1848-1850.
王永杰,孟令奎,赵春宇.基于Hilbert空间排列码的海量空间数据划分算法研究[J].武汉大学学报:信息科学版,2007,32(007):650-653.
维基百科.云计算.http://zh.wikipedia.org/zh-cn/%E4%BA%91%E8%AE%A1%E7%AE%97[EB/ OL],2010.
邬伦.地理信息系统,原理,方法和应用[M].北京:科学出版社,2000.
吴洪桥,张子平,贾文珏等.从Web Service到网格技术及在国土资源信息服务中的应用[J].国土资源信息化,2008(04):23-26.
夏江.基于PC集群系统的场地地震反应并行计算研究[D].博士学位论文,同济大学,2007.
肖心智,苏奋振,杜云艳等.WebGIS性能分析与优化[J].测绘与空间地理信息,2005,28(004):1-3.
小璇.乐港CEO陈博畅谈游戏云计算发展趋势.http://web.17173.com/content/2010-04-15/2010 0415111841072.shtml[EB/OL],2010.
谢钧,俞璐,吴乐南.一种改进的Split—Merge图像分割算法[J].计算机应用,2008,28(007):1744-1746.
谢文伟,李凤英.浅议第二次全国土地调查——与第一次全国土地调查之比较[J].科技信息(学术版),2007(5):55.
新华网.高科技手段土地调查的重要支撑——《第二次全国土地调查总体方案》系列解读之九.http://news.xinhuanet.com/fortune/2007-07/05/content_6333755.htm[EB/OL],2007. 徐志伟.互联网涅磐——正在浮现的网格技术[J].互联网世界,2002:22.
许欢.面向服务的土地资源空间信息多级语义网格研究[D].博士学位论文,浙江大学,2009.
许礼林,崔秉良,王小平等.城市规划国土管理的图文集成管理[J].测绘学报,1998,27(2):153-160.
严志明.基于网格技术的分布式GIS新技术研究[D].博士学位论文,浙江大学,2004.
杨刚,随玉磊.面向云计算平台自适应资源监测方法[J].计算机工程与应用,2009,45(029):14-17.
叶梓.简单要素模型并行化空间运算研究与实现[D].硕士学位论文,中国地质大学,2009.
尹小明.基于价值网的云计算商业模式研究[D].硕士学位论文,北京邮电大学,2009. 岳海斌,周庆俊,陈鸿志等.网格技术在国土资源信息化中的应用探讨[C].土地信息技术的创新与土地科学技术发展--2006年中国土地学会学术年会论文集.2006年中国土地学会学术年会.重庆,2006.
张发存,王忠,赵晓红等.遥感卫星图像几何粗校正的数据并行方法研究[J].JOURNAL OF COMPUTER RESEARCH AND DEVELOPMENT,2004,1:41.
张丰.面向网格的海量时空数据访问、集成与互操作研究[D].博士学位论文,浙江大学;,2007.
张季平,曾国荪,吴豪.高性能网格并行计算[J].计算机工程,2004,30(001):1-3.
张建梁.基于云计算的语义搜索引擎研究[D].硕士学位论文,复旦大学,2009.
张鹏.IBM:虚拟化是云计算关键能力[J].通信世界,2009(045):19.
张学明,施法中.分布式并行数据挖掘系统的研究与实现[J].计算机工程与应用,2002,38(004):198-200.
张振伦.虚拟化就是云计算的基石.http://topoint.com.cn/html/virtual/virnews/2009/04/232942.htm l[EB/OL],2009.
章毓晋.图像分割[M]:北京:科学出版社,2001.
赵春燕.云环境下作业调度算法研究与实现[D].硕士学位论文,北京交通大学,2009.
赵春宇.高性能并行GIS中矢量空间数据存取与处理关键技术研究[D].博士学位论文,武汉大学,2006a.
赵春宇,孟令奎,林志勇.一种面向并行空间数据库的数据划分算法研究[J].武汉大学学报:信息科学版,2006b,31(011):962-965.
赵元.云计算在港口行业中的应用研究[D].硕士学位论文,北京交通大学,2009.
浙江省国土资源厅.浙江省城镇地籍数据库建设技术规定(试行)[S],2002.
郑明玲,周海芳,刘衡竹等.遥感多图像配准中自动提取特征点的并行算法[J].COMPUTER,2004,26(1):45-48.
钟耳顺.土地信息系统建设中的若干问题(一)[J].国土资源信息化,2001(02):14-16.
钟绍波.基于动态负载均衡策略的网格任务调度优化模型和算法[J].计算机应用,2008,28(011):2867-2870.
周海芳,杜云飞,杨学军等.基于互信息的遥感图像区域配准并行算法的研究与实现[J].中国图象图形学报,2010(001):174-180.
周海芳,刘光明,郑明玲等.遥感图像自动配准的串行与并行策略研究[J].国防科技大学学报,2004b,26(002):56-61.
周海芳,唐宇,何凯涛等.基于小波的遥感图像全局配准算法研究及其并行实现[J].自动化学报,2004a,30(006):880-889.
周静,周海芳,唐玉华.多模遥感图像高精度配准并行算法研究与实现[C].图像图形技术研究与应用2009——第四届图像图形技术与应用学术会议论文集,2009.
Aboulnaga A,Salem K,Soror A A et al.Deploying database appliances in the cloud[J].Data Engineering,2009,32(1):13.
Abugov D.Oracle Spatial Partitioning: Best Practices (an Oracle White Paper)[M]:Oracle Inc.,2004.
Achalakul T,Taylor S.A distributed spectral-screening PCT algorithm[J].Journal of Parallel and Distributed Computing,2003,63(3):373-384.
Amazon.Amazon elastic compute cloud.http://aws.amazon.com/ec2/[EB/OL].2010. Anderson T E,Culler D E,Patterson D A.A case for NOW (Networks of Workstations)[J].IEEE micro,1995,15(1):54-64.
Apache.Welcome to Apache Hadoop!.http://hadoop.apache.org/[EB/OL].2010.
Armbrust M , Fox A , Griffith R et al . Above the clouds: A berkeley view of cloud computing[J] . EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28,2009.
Attiya H,Welch J.Distributed computing: fundamentals, simulations, and advanced topics[M]:Wiley-Interscience,2004.
Baker M.Cluster computing white paper[M],2000.
Baker M,Fox G,Yau H.Cluster computing review[M],1995.
Balasubramaniam M,Barker K,Banicescu I et al.A novel dynamic load balancing library for cluster computing[C]:IEEE,2004:346-353.
Baranski B,Sch Ffer B,Redweik R.Geoprocessing in the Clouds[C].Free and Open Source Software for Geospatial Conference.Sydney, Australia,2009.
Barham P,Dragovic B,Fraser K et al.Xen and the art of virtualization[C].Proceedings of the nineteenth ACM symposium on Operating systems principles:ACM,2003:164-177.
Batcher K E.Design of a massively parallel processor[J].IEEE Transactions on Computers,1980:836-840.
Berman F,Fox G,Hey A.Grid computing: making the global infrastructure a reality[M]:John Wiley & Sons Inc,2003.
Bertino E,Paci F,Ferrini R et al.Privacy-preserving digital identity management for cloud computing[J].Data Engineering,2009:21.
Bertogna M,Cirinei M.Response-time analysis for globally scheduled symmetric multiprocessor platforms[C].28th IEEE Real-Time Systems Symposium (RTSS).
Bertogna M,Cirinei M.Response-Time Analysis for Globally Scheduled Symmetric Multiprocessor Platforms[C].28th IEEE International Real-Time Systems Symposium, 2007. RTSS 2007:IEEE Computer Society Washington, DC, USA,2007:149-160.
Beyer K,Ercegovac V,Krishnamurthy R et al.Towards a scalable enterprise content analytics platform[J].IEEE Data Eng. Bull,2009,32(1):28-35.
Blower J D . GIS in the cloud: implementing a Web Map Service on Google App Engine[C].Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application:ACM,2010:1-4.
Boss G,Malladi P,Quan D et al.Cloud computing. IBM White Paper.http://download.boulder.ibm .com/ibmdl/pub/software/dw/wes/hipods/Cloud_computing_wp_final_8Oct.pdf[EB/OL]. Boulon J,Konwinski A,Qi R et al.Chukwa, a large-scale monitoring system[J].Cloud Computing and its Applications,2008:1-5.
Brauner J,Foerster T,Schaeffer B et al.Towards a Research Agenda for Geoprocessing Services[C].12th AGILE International Conference on Geographic Information Science,2009.
Brinkhoff T,Kriegel H P,Seeger B.Efficient processing of spatial joins using R-trees[J].ACM SIGMOD Record,1993,22(2):237-246.
Bryant R E.Data-intensive supercomputing: The case for DISC[J].School of Computer Science, Carnegie Mellon University, Tech. Rep. Technical Report CMU-CS-07-128,2007.
Burrows M.The Chubby lock service for loosely-coupled distributed systems[C]:USENIX Association,2006:350.
Buyya R.High Performance Cluster Computing: Architectures and Systems, Volume 1[J].Prentice Hall PTR,1999,82:327-350.
Buyya R,Yeo C S,Venugopal S.Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities[C].10th IEEE International Conference on High Performance Computing and Communications, 2008. HPCC'08,2008:5-13.
Capra L,Mascolo C,Zachariadis S et al.Towards a mobile computing middleware: a synergy of reflection and mobile code techniques[C]:Published by the IEEE Computer Society,2001:148.
Carr N.The Big Switch: Rewiring the World, From Edison to Google[M].London:W. W. Norton & Company,2008.
Chalermwat P.High performance automatic image registration for remote sensing[D].Doctor,Citeseer,1999.
Chang F,Dean J,Ghemawat S et al.Bigtable: A distributed storage system for structured
data[J].ACM Transactions on Computer Systems (TOCS),2008,26(2):4.
Charles J.Middleware moves to the forefront[J].Computer,1999,32(5):17-19.
Chau S C,Fu A W C.Load balancing between computing clusters[C]:IEEE,2003:548-551.
Chen Q,Wang L,Shang Z.MRGIS: A MapReduce-Enabled High Performance Workflow System for GIS[C].IEEE Fourth International Conference on eScience, 2008. eScience'08,2008:646-651.
Chien A,Calder B,Elbert S et al.Entropia: architecture and performance of an enterprise desktop grid system[J].Journal of Parallel and Distributed Computing,2003,63(5):597-610.
Cook S,Dwork C,Reischuk R.Upper and lower time bounds for parallel random access machines without simultaneous writes[J].SIAM Journal on Computing,1986,15:87.
Crockett T W.An introduction to parallel rendering[J].Parallel Computing,1997,23(7):819-843.
Crookes D.Architectures for high performance image processing[J].The future Journal of Systems Architecture,1999,45(10):739-748.
Dean J,Ghemawat S.Distributed programming with Mapreduce[J].Beautiful Code. Sebastopol: OReilly Media, Inc,2007:371-384.
Dean J,Ghemawat S.MapReduce: Simplified data processing on large clusters[J].Communications of the ACM,2008,51(1):107-113.
Dean J,Ghemawat S.Mapreduce: a flexible data processing tool[J].Communications of the ACM,2010,53(1):72-77.
DeWitt D J,Stonebraker M.MapReduce: A major step backwards[J].The Database Column,2008,1.
Dhodhi M K,Saghri J A,Ahmad I et al.D-ISODATA: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations[J].Journal of Parallel and Distributed Computing,1999,59(2):280-301.
Django.The Django Framework.http://www.djangoproject.com/[EB/OL].2010-6-10.
Enslow P H.What is" Distributed" Data Processing System?[J].Computer ,1978,11(1):13-21.
Fang L,Huang X,Pan N et al.A New Dynamic Tile Caching Method for WebGIS[C].the International Conference onMultimedia Technology (ICMT):IEEE,2010:1-4.
Fenn J,Raskino M,Gammage B .Gartner's Hype Cycle Special Report for 2009[R] ,http://www.gartner.com/DisplayDocument?ref=g_search&id=1108412&subref=simplesearch,2009.
Fleury M,Self R P,Downton A C.Development of a fine-grained parallel Karhunen-Loève transform[J].Journal of Parallel and Distributed Computing,2004,64(4):520-535.
Fortune S,Wyllie J.Parallelism in random access machines[C]:ACM,1978:114-118.
Foster I.What is The Grid? A Three Point Checklist[J].GRIDToday,2002b,01(06):22-25.
Foster I,Kesselman C.The grid: blueprint for a new computing infrastructure[M].San Fransisco, CA, USA:Morgan Kaufmann Publishers Inc.,2004.
Foster I,Kesselman C,Nick J et al.The physiology of the grid: An open grid services architecture for distributed systems integration[C]:Edinburgh,2002a:1-5.
Foster I,Kesselman C,Tuecke S.The Anatomy of the Grid: Enabling Scalable Virtual Organizations[J].International Journal of High Performance Computing Applications,2001,15(3):200-222.
Foster I,Zhao Y,Raicu I et al.Cloud computing and grid computing 360-degree compared[C].Grid Computing Environments Workshop, 2008. GCE'08,2008:1-10.
Fu X,Wang D,Zheng W et al.GPR-Tree: a global parallel index structure for multiattribute declustering on cluster of workstations[C].Advances in Parallel and Distributed Computing:IEEE,2002:300-306.
Ganapathi A,Kuno H,Dayal U et al.Predicting multiple metrics for queries: Better decisions enabled by machine learning[C]:Citeseer,2009.
Ghemawat S,Dean J.MapReduce: Simplified Data Processing on Large Clusters[J].Usenix SDI,2004:137-150.
Ghemawat S,Gobioff H,Leung S T.The Google file system[J].ACM SIGOPS Operating Systems Review,2003,37(5):43.
Gillett F E,Brown G E,Staten J et al.The new tech ecosystems of cloud, cloud services, and cloud computing[R],Forrester Research Report,2008.
Goldberg R P.Survey of virtual machine research[J].IEEE Computer,1974,7(6):34-45.
Google.Google App Engine.http://appengine.google.com/[EB/OL].2010.
Google.Google Maps JavaScript API V3.http://code.google.com/intl/zh-CN/apis/maps/documen tation/javascript/[EB/OL].2010.
Greggo A.Cloud computing in the Enterprise: An Overview[C].Enterprise Computing Conference,2009.
Guo J , Bhuyan L N . Load balancing in a cluster-based web server for multimedia applications[J].IEEE Transactions on Parallel and Distributed Systems,2006,17(11):1321-1334.
Hastorun D,Jampani M,Kakulapati G et al.Dynamo: amazon s highly available key-value store[C]:Citeseer,2007:205-220.
Henry A.Using Google Earth for Internet GIS[J],2009.
Hewitt C.ORGs for scalable, robust, privacy-friendly client Cloud Computing[J].IEEE internet computing,2008,12(5):96-99.
Hewlett-Packard.HP Integrated Lights-Out 2 User Guide[R],2009.
Hibler M , Ricci R , Stoller L et al . Large-scale virtualization in the emulab network testbed[C].USENIX 2008 Annual Technical Conference on Annual Technical Conference:USENIX Association,2008:113-128.
Houston I,King S.CICS project report experiences and results from the use of Z in IBM[C]:Springer,1991:588-596.
Hwang K . Advanced computer architecture: parallelism, scalability, programmability[M] :McGraw-Hill New York,1993.
Ibrahim M A M,Xinda L.Performance of dynamic load balancing algorithm on cluster of workstations and PCs[C]:IEEE,2003b:44-47.
Ibrahim M,Xinda L.Utilization of cluster of PCs for the study of dynamic load balancing[C]:IEEE,2003a:335-337.
Isard M,Budiu M,Yu Y et al.Dryad: distributed data-parallel programs from sequential building blocks[J].ACM SIGOPS Operating Systems Review,2007,41(3):72.
ISO.Geographic information-Services[S].http://portal.opengeospatial.org/files/?artifact_id=1221,2001-09-14.
Ji-hong G,Shui-geng Z,Fu-ling B et al.Building Distributed Web GIS: A Mobile-Agent Based Approach[J].Wuhan University Journal of Natural Sciences,武汉大学学报(自然科学版.英文版),2001,6(2):474-481.
Joyent.Joyent Smart Computing.http://www.joyent.com/[EB/OL].2010-7-10.
Kalla R,Sinharoy B,Tendler J M.IBM Power5 chip: A dual-core multithreaded processor[J].IEEE micro,2004,24(2):40-47.
Kamel I,Faloutsos C.Hilbert R-tree: An improved R-tree using fractals[C].the 20th VLDB Conference.Santago,Chile:Citeseer,1994.
Keahey K,Freeman T.Contextualization: Providing one-click virtual clusters[C].Fourth IEEE International Conference on eScience:IEEE,2008:301-308.
Kepner J.A Multi-Threaded Fast Convolver for Dynamically Parallel Image Filtering[J].Journal of Parallel and Distributed Computing,2001,63(3):360-372.
Kepp A.Caching Dynamic Servers.http://opengeo.org/products/coredevelopment/geowebcache/tile expiration/[EB/OL].2009.
Kivity A,Kamay Y,Laor D et al.KVM: the Linux virtual machine monitor[C],2007:225-230.
Kumar V.Introduction to parallel computing[M].Boston, MA, USA:Addison-Wesley Longman Publishing Co., Inc. ,2002.
Lai K,Rasmusson L,Adar E et al.Tycoon: An implementation of a distributed, market-based resource allocation system[J].Multiagent and Grid Systems,2005,1(3):169-182.
Lee O,Anshel M,Chung I.Design of an efficient load balancing algorithm on distributed networks by employing symmetric balanced incomplete block design[C]:IEE Proc. Commun,2004:535-538.
Lei F,Shen-jun Y,Ting L et al.A Cloud Computing Application in Land Resources Information Management[C]:IEEE,2010:388-393.
Lenk A,Klems M,Nimis J et al.What's inside the Cloud? An architectural map of the Cloud landscape[C]:IEEE Computer Society,2009:23-31.
Lenoski D,Laudon J,Gharachorloo K et al.The stanford dash multiprocessor[J].Computer,1992,25(3):79.
Leutenegger S T,Lopez M A,Edgington J.STR: A simple and efficient algorithm for R-tree packing[C].Proceedings. 13th International Conference on Data Engineering:IEEE,2002:497-506.
Litty L,Lagar-Cavilla H A,Lie D.Computer Meteorology: Monitoring Compute Clouds[C],2009.
Liu H,Orban D.Gridbatch: Cloud computing for large-scale data-intensive batch applications[C],2008:295-305.
LVSKB.Load balancing.http://kb.linuxvirtualserver.org/wiki/Load_balancing#Computing_Load_ Balancing[EB/OL].2008.
McKeown N,Anderson T,Balakrishnan H et al.OpenFlow: enabling innovation in campus networks[J].ACM SIGCOMM Computer Communication Review,2008,38(2):69-74.
Microsoft.Windows Azure Platform.http://www.microsoft.com/windowsazure/[EB/OL].2010-7 -10.
Milojicic D S,Kalogeraki V,Lukose R et al.Peer-to-peer computing[M]:Citeseer,2002.
Milojicic D S,LaForge W,Chauhan D.Mobile objects and agents (MOA)[J].Distributed Systems Engineering,1998,5:214.
Moretti C,Bulosan J,Thain D et al.All-pairs: An abstraction for data-intensive cloud computing[C]:Citeseer,2008.
Nativi S,Ramamurthy M,Woolf A.Towards Earth and Space Science digital infrastructures: network, computing and data services[J].editorial of the International Journal of Digital Earth,2009,2(Supplement 1):1-2.
Newman A,Li Y F,Hunter J.Scalable Semantics–the Silver Lining of Cloud Computing[C],2008:111-118.
Nurmi D,Wolski R,Grzegorczyk C et al.Eucalyptus: A technical report on an elastic utility computing archietcture linking your programs to useful systems[R],Computer Science Tech.rep,2008.
Nurmi D,Wolski R,Grzegorczyk C et al.The eucalyptus open-source cloud-computing system[C]:IEEE Computer Society,2009:124-131.
Nyland L,Prins J,Goldberg A et al.A refinement methodology for developing data-parallel applications[C]:Springer,1996:145-150.
Oberheide J,Cooke E,Jahanian F.Cloudav: N-version antivirus in the network cloud[C]:USENIX Association,2008:91-106.
OpenIDFoundation.OpenID[J].Retrieved November,2008a,24:2008.
OpenIDFoundation.OpenID homepage.http://openid.net/[EB/OL].2010-7-10.
Pavlo A,Paulson E,Rasin A et al.A comparison of approaches to large-scale data analysis[C]:ACM,2009:165-178.
Pearson S.Taking account of privacy when designing cloud computing services[C]:IEEE Computer Society,2009:44-52.
Pfister G F.In search of clusters: the coming battle in lowly parallel computing[M].Upper Saddle River, NJ, USA:Prentice-Hall, Inc. ,1995.
Plaza A J.Parallel techniques for information extraction from hyperspectral imagery using heterogeneous networks of workstations[J].Journal of Parallel and Distributed Computing,2008,68(1):93-111.
P?idal M P,?abi?ka P.Tiles as an approach to on-line publishing of scanned old maps, vedute and other historical documents[J].e-Perimetron,2008,3(1):10-21.
Quinn M J.Parallel computing: theory and practice[M]:McGraw-Hill, Inc. New York, NY, USA,1994.
Ranka S,Won Y,Sahni S.Programming a hypercube multicomputer[J].Software, IEEE,2002,5(5):69-77.
Reid-Miller M.List ranking and list scan on the Cray C-90[C]:ACM,1994:104-113. Ritter G X,Gader P D.Image algebra techniques for parallel image processing* 1[J].Journal of Parallel and Distributed Computing,1987,4(1):7-44.
Satoh I.MobileSpaces: A framework for building adaptive distributed applications using a hierarchical mobile agent system[C]:Published by the IEEE Computer Society,2000:161.
Schnitzer B,Leutenegger S T.Master-Client R-trees: A New Parallel R-tree Architecture[C].the 11th International Conference on Scientific and Statistical Database Management:IEEE Computer Society,1999:68.
Schwiegelshohn U,Badia R M,Bubak M et al.Perspectives on grid computing[J].Future Generation Computer Systems,2010,26(8):1104-1115.
Shekhar Shashi,Chawla Sanjay.空间数据库[M].北京:机械工业出版社,2004.
Silberschatz A.,Korth H. F.,Sudarshan S..数据库系统概念[M].北京:机械工业出版社,2000.
Sims K.IBM introduces ready-to-use cloud computing collaboration services get clients started with cloud computing.http://www-03.ibm.com/press/us/en/pressrelease/22613.wss[EB/OL].
Sit H Y,Ho K S,Leong H V et al.An adaptive clustering approach to dynamic load balancing[C]:IEEE,2004:415-420.
Sotomayor B,Montero R,Llorente I M et al.Capacity leasing in cloud systems using the opennebula engine[J].Cloud Computing and Applications,2008,2008.
Srikantaiah S,Kansal A,Zhao F.Energy aware consolidation for cloud computing[C],2008.
Stonebraker M,Abadi D,DeWitt D J et al.MapReduce and parallel DBMSs: friends or foes?[J].Communications of the ACM,2010,53(1):64-71.
Stra Er M,Baumann J,Hohl F.Mole-a java based mobile agent system[C],1996.
Subhlok J,Vondran G.Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations[J].Journal of Parallel and Distributed Computing,2000,60(3):297-319.
Sumanth J V,Swanson D R,Jiang H.Adaptive Load Balancing for Long-Range MD Simulations in A Distributed Environment[C].Proceedings of International Conference on Parallel Processing,2006:135-146.
SunMicrosystems.Project Caroline.http://labs.oracle.com/projects/caroline/[EB/OL].2010-6-20. Sun公司.云计算入门指南.http://cn.sun.com/offers/docs/sun_cloudcomputing_chinese.pdf.[EB/OL ],2009.
Tuszy Nacute J.Parallelizing the construction of indirect access arrays for shared-memory machines[J].Communications in Numerical Methods in Engineering,1998,14(8):773-781.
Vaquero L M,Rodero-Merino L,Caceres J et al.A break in the clouds: towards a cloud definition[J].ACM SIGCOMM Computer Communication Review,2008,39(1):50-55.
VMware.VMware Infrastructure.http://www.vmware.com.cn/pproducts/vi[EB/OL]. WANG B,HORINOKUCHI H,KANEKO K et al.Implementation and Evaluation of Parallel R-tree on Parallel Object Database System ShusseUo.[J].Transactions of Information Processing Society of Japan,1999,40:92-103.
Wang B,Horinokuchi H,Kaneko K et al.Parallel R-tree search algorithm on DSVM[C].the 6th International Conference on Database Systems for Advanced Applications:IEEE,2002:237-244.
Wang L,Tao J,Kunze M et al.The Cumulus Project: Build a scientific cloud for a data center[J].Cloud Computing and its Applications,2008a.
Wang L,Tao J,Kunze M et al.Scientific cloud computing: Early definition and experience[C].10th IEEE International Conference on High Performance Computing and Communications, 2008. HPCC'08,2008b:825-830.
Wang Y,Wang S,Zhou D.Retrieving and Indexing Spatial Data in the Cloud Computing Environment[J].Cloud Computing,2009:322-331.
Weiss A.Computing in the clouds[J].NetWorker,2007,11(4):16-25.
Wooldridge M,Jennings N.Agent theories, architectures, and languages: a survey[J].Intelligent agents,1995:1-39.
Writer E.Tiles on a Cloud-Cloud computing and ArcGIS Server deliver a thrifty solution[J],2010. Wu M Y,Shu W.A load-balancing algorithm for n-cubes[C]:IEEE,2002:148-155.
Wu S,Wu K L.An indexing framework for efficient retrieval on the cloud[J].Data Engineering,2009:75.
XenSource.Xen.http://cn.opensuse.org/Xen_Virtual_Machine_Overview[EB/OL]. Xia Y,Chen S,Korgaonkar V.Load balancing with multiple hash functions in peer-to-peer networks[C].Proceedings of the 12th International Conference on Parallel and Distributed Systems-Volume 1:IEEE Computer Society,2006:411-420.
Xian F,Srisa-an W,Jiang H.Contention-aware scheduler: unlocking execution parallelism in multithreaded java programs[C].ACM SIGPLAN Notices 10:ACM,2008:163-180.
Xiao Y,Guizani M.Optimal paging load balance with total delay constraint in macrocell-microcell hierarchical cellular networks[J].Wireless Communications, IEEE Transactions on,2006,5(8):2202-2209.
Yan W,Wu E.Toward Automatic Discovery of Malware Signature for Anti-virus Cloud Computing[J].Complex Sciences,2009:724-728.
Yeo C S,De Assuncao M D,Yu J et al.Utility Computing and Global Grids[J].Arxiv preprint cs/0605056,2006.
Youseff L,Butrico M,Da Silva D.Toward a unified ontology of cloud computing[C].Grid Computing Environments Workshop, 2008. GCE'08,2008:1-10.
Yu Y,Gunda P K,Isard M.Distributed aggregation for data-parallel computing: interfaces and implementations[C]:ACM,2009:247-260.
Yu Y,Isard M,Fetterly D et al.DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language[C].Symposium on Operating System Design and Implementation(OSDI).San Diego,2008.
Zhang Q,Riska A,Sun W et al.Workload-aware load balancing for clustered web servers[J].IEEE Transactions on Parallel and Distributed Systems,2005,16(3):219-233.
Ziavras S G,Meer P.Adaptive multiresolution structures for image processing on parallel computers[J].Journal of Parallel and Distributed Computing,1994,23(3):475-483.
百度百科.云计算.http://baike.baidu.com/view/1316082.htm[EB/OL],2009a.
百度百科.云安全.http://baike.baidu.com/view/1725454.htm?fr=ala0_1_1[EB/OL],2009b.
百度百科.计算机集群.http://baike.baidu.com/view/302477.htm[EB/OL],2010.
蔡孟裔.新编地图学教程[M]:高等教育出版社,2000.
陈国良.并行算法实践[M].北京:高等教育出版社,2004.
陈国良.并行算法研究进展[J].中国计算机学会通讯,2005,1(2):18-21.
陈国良,吴俊敏,章锋.并行计算机体系结构[M].北京:高等教育出版社,2002.
陈海波.云计算平台可信性增强技术的研究[D].博士学位论文,复旦大学,2008.
陈康.云计算后台大规模数据处理技术探讨[J].电信工程技术与标准化,2009b(011):12-16.
陈康,郑纬民.云计算:系统实例与研究现状[J].Journal of Software,2009a,20(5):1337-1348.
陈能成,龚健雅,朱欣焰等.基于J2EE的分布式GIS研究[J].测绘学报,2003,32(002):158-163.
陈全,邓倩妮.云计算及其关键技术[J].计算机应用,2009,29(9):2562-2567.
陈育春.Google Maps API开发大全[M].北京:机械工业出版社,2010.
陈志荣,李昭,刘婷.面向移动用户的空间信息网格服务模型[J].浙江大学学报:理学版,2010,37(005):577-582.
戴立乾,陈娜.浅议云计算时代下GIS的发展[J].安徽农业科学,2009,37(031):15556-15557.
戴元顺.云计算技术简述[J].信息通信技术,2010,4(002):29-35.
邓自立.云计算中的网络拓扑设计和Hadoop平台研究[D].硕士学位论文,中国科学技术大学,2009.
董开坤,胡铭曾,方滨兴等.一个基于图像代数的并行图像处理环境[J].计算机研究与发展,2004,41(001):201-206.
方金云,何建邦.网格GIS体系结构及其实现技术[J].地球信息科学,2002,4(004):36-42.
方金云,何建邦.并行栅格数据处理网格服务节点软件的关键技术[J].地球信息科学,2004,6(001):17-21.
付志超.基于Map/Reduce的分布式智能搜索引擎框架研究[D].硕士学位论文,武汉理工大学,2008.
龚健雅.地理信息系统基础[M].北京:科学出版社,2001.
龚雪晶,慈林林,姚康泽.基于邻域信息的遥感图像模糊聚类及并行算法设计[J].计算机应用,2007,27(010):2512-2514.
桂小林.网格技术导论[M].北京:北京邮电大学出版社,2004:4-6.
国土资源部.城镇地籍数据库标准(行业试行)[S],2001.
国土资源部.土地基本术语[S],2003.
国土资源部.城镇地籍数据库标准[S],2007.
国务院.国务院关于开展第二次全国土地调查的通知国发[2006]38号.http://www.gov.cn/zwgk/2006-12/20/content_474183.htm[EB/OL],2006.
国务院第二次全国土地调查领导小组办公室.第二次全国土地调查总体方案[S],2007-6-5.
国务院第二次全国土地调查领导小组办公室.第二次全国土地调查数据库更新技术规范(试行)[S],2009.
韩琼.土地管理信息化方案与策略研究[D].博士学位论文,中国地质大学,2003.
韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(006):91-94.
何建农.网格计算技术在土地信息管理中的应用[C].Web技术与应用.全国第五次程序设计
语言发展与教学学术会议、第三届全国Web信息系统及其应用学术会议暨全国首届语义Web与本体论学术研讨会.南京,2006:219-222.
何亚文,杜云艳,苏奋振等.利用空间信息网格的海流场远程可视化[J].武汉大学学报:信息科学版,2010,35(003):350-352.
胡玥,高庆狮,高小宇.串行算法并行化基础[M].北京:科学出版社,2008.
黄德霖,鲍家伟.浅谈土地信息系统标准化问题[J].浙江国土资源,2005(06):47-49.
黄国满,郭建峰.分布式并行遥感图像处理中的数据划分[J].遥感信息,2001,2:9-12.
黄舟.网格环境下空间计算任务处理技术研究[D].博士学位论文,北京大学,2009.
纪俊.一种基于云计算的数据挖掘平台架构设计与实现[D].硕士学位论文,青岛大学,2009.
金江军,潘懋.网格技术及其在国土资源信息化中的应用探讨[J].国土资源信息化,2005(01):
金哲凡,杨建,石教英.分布式并行绘制系统中几何指令流压缩的研究与实现[J].计算机辅助设计与图形学学报,2002,14(009):824-828.
靳华中,孟令奎,王显.集群环境下并行GIS的体系结构设计[J].地理空间信息,2005,3(5).
景晓军,蔡安妮.一种基于二维最大类间方差的图像分割算法[J].通信学报,2001,22(004):71-76.
李德仁,宾洪超.国土资源网格管理平台的框架设计与实现[J].测绘科学,2008a,33(01):8-9.
李德仁,宾洪超,邵振峰.国土资源网格化管理与服务系统的设计与实现[J].武汉大学学报(信息科学版),2008b,33(01):1-6.
李德仁,朱欣焰,龚健雅.从数字地图到空间信息网格——空间信息多级网格理论思考[J].武汉大学学报(信息科学版),2003,28(06):642-650.
李国杰.信息服务网络——第三代Internet[R].亚太经合组织计交会科技论坛报告集.中国,2001:150-152.
李松伟.盛大AMD云计算联合实验室落户上海.http://tech.qq.com/a/20100419/000271.htm[EB/ OL],2010.
李霞,张基宏.基于遗传算法的任务分配问题求解[J].数据采集与处理,1999,14(003):293-297.
李夏英.浙江省土地管理体制改革若干问题的研究[D].硕士学位论文,华东师范大学,2006.
刘凡茂.基于云计算的乡镇卫生院信息化研究[D].硕士学位论文,中南大学,2009.
刘鹏.网格概念的界定.http://www.chinagrid.net/grid/paperppt/GridConcept.pdf[EB/OL],2003. 刘鹏.云计算[M].北京:电子工业出版社,2010.
刘鹏程.云计算中虚拟机动态迁移的研究[D].硕士学位论文,复旦大学,2009.
刘晓沐,岳丽华,陈博等.遥感图像目标识别的并行处理方法[J].计算机应用,2007,27(009):2123-2125.
刘旭辉,韩冀中,贺劲等.基于集群系统的空间数据并行处理策略研究[J].高技术通讯,2009,19(010):991-997. 刘异,呙维,江万寿等.一种基于云计算模型的遥感处理服务模式研究与实现[J].计算机应用研究,2009(009):3428-3431.
刘勇,朱国强.基于TCP/IP的网络并行计算的实现[J].计算机工程,2000,26(008):187-189.
刘志军,丁明跃,周成平等.基于并行遗传算法的图像超分辨率复原[J].中国图象图形学报: A 辑,2004,9(001):62-68.
陆松,刘光明.分布共享存储的遥感图像并行预处理系统结构研究[J].计算机工程与科学,2004,26(010):56-59.
骆剑承,周成虎,蔡少华等.基于中间件技术的网格GIS体系结构[J].地球信息科学,2002,4(003):17-25.
吕捷,张天序,张必银.MPI并行计算在图像处理方面的应用[J].红外与激光工程,2004,33(005):496-499.
马荣华,黄杏元.大型GIS海量数据分布式组织与管理[J].南京大学学报:自然科学版,2003,39(006):836-843.
马维峰,王晓蕊,高松峰等.基于服务器动态缓存和Ajax技术的WebGIS开发[J].测绘科学,2008,33(005):204-205.
钱巍,吕晶,李晗静.一种机群系统下的并行图像处理环境[J].哈尔滨师范大学自然科学学报,2005,21(002):61-65.
沈占锋,骆剑承,陈秋晓等.基于MPI的遥感影像高效能并行处理方法研究[J].中国图象图形学报,2007,12(012):2132-2136.
宋关福.GIS软件:从纸空间到真空间的蜕变.http://www.supermap.com.cn/sup/xwtxpage. asp?orderID=140[EB/OL],2009.
苏日娜,王宇.基于免疫遗传算法的负载均衡策略[J].计算机应用,2010(010):2595-2597.
孙宏元.基于HPC的多分辨空间信息应用基础平台关键技术研究[D].博士学位论文,西安电子科技大学,2006.
陶卫中.MODIS数据预处理的并行算法设计[D].硕士学位论文,华中科技大学,2007.
滕龙妹.土地资源时空数据网格服务模型及其实现方法[D].博士学位论文,浙江大学;,2008.
王鄂,李铭.云计算下的海量数据挖掘研究[J].现代计算机:下半月版,2009(011):22-25.
王家耀,孙庆辉,吴明光等.面向智能空间信息服务的网格GIS节点构建[J].武汉大学学报: 信息科学版,2009,34(001):1-6.
王茜蒨,彭中,刘莉.一种基于自适应阈值的图像分割算法[J].北京理工大学学报,2003,23(004):521-524.
王升平,李青.数据复制管理中共享缓存数据流的实现[J].计算机工程,2007,33(20):83.
王怡,周明全,耿国华.基于简化Mumford-Shah模型的水平集图像分割算法[J].计算机应用,2006,26(008):1848-1850.
王永杰,孟令奎,赵春宇.基于Hilbert空间排列码的海量空间数据划分算法研究[J].武汉大学学报:信息科学版,2007,32(007):650-653.
维基百科.云计算.http://zh.wikipedia.org/zh-cn/%E4%BA%91%E8%AE%A1%E7%AE%97[EB/ OL],2010.
邬伦.地理信息系统,原理,方法和应用[M].北京:科学出版社,2000.
吴洪桥,张子平,贾文珏等.从Web Service到网格技术及在国土资源信息服务中的应用[J].国土资源信息化,2008(04):23-26.
夏江.基于PC集群系统的场地地震反应并行计算研究[D].博士学位论文,同济大学,2007.
肖心智,苏奋振,杜云艳等.WebGIS性能分析与优化[J].测绘与空间地理信息,2005,28(004):1-3.
小璇.乐港CEO陈博畅谈游戏云计算发展趋势.http://web.17173.com/content/2010-04-15/2010 0415111841072.shtml[EB/OL],2010.
谢钧,俞璐,吴乐南.一种改进的Split—Merge图像分割算法[J].计算机应用,2008,28(007):1744-1746.
谢文伟,李凤英.浅议第二次全国土地调查——与第一次全国土地调查之比较[J].科技信息(学术版),2007(5):55.
新华网.高科技手段土地调查的重要支撑——《第二次全国土地调查总体方案》系列解读之九.http://news.xinhuanet.com/fortune/2007-07/05/content_6333755.htm[EB/OL],2007. 徐志伟.互联网涅磐——正在浮现的网格技术[J].互联网世界,2002:22.
许欢.面向服务的土地资源空间信息多级语义网格研究[D].博士学位论文,浙江大学,2009.
许礼林,崔秉良,王小平等.城市规划国土管理的图文集成管理[J].测绘学报,1998,27(2):153-160.
严志明.基于网格技术的分布式GIS新技术研究[D].博士学位论文,浙江大学,2004.
杨刚,随玉磊.面向云计算平台自适应资源监测方法[J].计算机工程与应用,2009,45(029):14-17.
叶梓.简单要素模型并行化空间运算研究与实现[D].硕士学位论文,中国地质大学,2009.
尹小明.基于价值网的云计算商业模式研究[D].硕士学位论文,北京邮电大学,2009. 岳海斌,周庆俊,陈鸿志等.网格技术在国土资源信息化中的应用探讨[C].土地信息技术的创新与土地科学技术发展--2006年中国土地学会学术年会论文集.2006年中国土地学会学术年会.重庆,2006.
张发存,王忠,赵晓红等.遥感卫星图像几何粗校正的数据并行方法研究[J].JOURNAL OF COMPUTER RESEARCH AND DEVELOPMENT,2004,1:41.
张丰.面向网格的海量时空数据访问、集成与互操作研究[D].博士学位论文,浙江大学;,2007.
张季平,曾国荪,吴豪.高性能网格并行计算[J].计算机工程,2004,30(001):1-3.
张建梁.基于云计算的语义搜索引擎研究[D].硕士学位论文,复旦大学,2009.
张鹏.IBM:虚拟化是云计算关键能力[J].通信世界,2009(045):19.
张学明,施法中.分布式并行数据挖掘系统的研究与实现[J].计算机工程与应用,2002,38(004):198-200.
张振伦.虚拟化就是云计算的基石.http://topoint.com.cn/html/virtual/virnews/2009/04/232942.htm l[EB/OL],2009.
章毓晋.图像分割[M]:北京:科学出版社,2001.
赵春燕.云环境下作业调度算法研究与实现[D].硕士学位论文,北京交通大学,2009.
赵春宇.高性能并行GIS中矢量空间数据存取与处理关键技术研究[D].博士学位论文,武汉大学,2006a.
赵春宇,孟令奎,林志勇.一种面向并行空间数据库的数据划分算法研究[J].武汉大学学报:信息科学版,2006b,31(011):962-965.
赵元.云计算在港口行业中的应用研究[D].硕士学位论文,北京交通大学,2009.
浙江省国土资源厅.浙江省城镇地籍数据库建设技术规定(试行)[S],2002.
郑明玲,周海芳,刘衡竹等.遥感多图像配准中自动提取特征点的并行算法[J].COMPUTER,2004,26(1):45-48.
钟耳顺.土地信息系统建设中的若干问题(一)[J].国土资源信息化,2001(02):14-16.
钟绍波.基于动态负载均衡策略的网格任务调度优化模型和算法[J].计算机应用,2008,28(011):2867-2870.
周海芳,杜云飞,杨学军等.基于互信息的遥感图像区域配准并行算法的研究与实现[J].中国图象图形学报,2010(001):174-180.
周海芳,刘光明,郑明玲等.遥感图像自动配准的串行与并行策略研究[J].国防科技大学学报,2004b,26(002):56-61.
周海芳,唐宇,何凯涛等.基于小波的遥感图像全局配准算法研究及其并行实现[J].自动化学报,2004a,30(006):880-889.
周静,周海芳,唐玉华.多模遥感图像高精度配准并行算法研究与实现[C].图像图形技术研究与应用2009——第四届图像图形技术与应用学术会议论文集,2009.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |