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1.
当前云计算的发展已能支持高性能的地理空间服务,比如在数字城市和电子商务等行业。Apache基金支持下的开源软件框架Hadoop,可以用来构建一个云环境的集群用来存储和处理高性能的地理空间数据。开放地理空间联盟(OGC)的Web三维服务(W3DS)就是这样一个很好的三维的地理空间数据服务标准。在标准的云计算环境下将是一个更好的应用示范。基于此,本文研究了OGC的W3DS服务在云计算环境下的实验结果。实验采用Apache的Hadoop框架作为三维地理空间信息服务实验展示的基础。实验结果对展示高性能的三维地理空间信息提供了有价值的参考。 相似文献
2.
《International Journal of Digital Earth》2013,6(4):383-403
Abstract Geospatial simulation models can help us understand the dynamic aspects of Digital Earth. To implement high-performance simulation models for complex geospatial problems, grid computing and cloud computing are two promising computational frameworks. This research compares the benefits and drawbacks of both in Web-based frameworks by testing a parallel Geographic Information System (GIS) simulation model (Schelling's residential segregation model). The parallel GIS simulation model was tested on XSEDE (a representative grid computing platform) and Amazon EC2 (a representative cloud computing platform). The test results demonstrate that cloud computing platforms can provide almost the same parallel computing capability as high-end grid computing frameworks. However, cloud computing resources are more accessible to individual scientists, easier to request and set up, and have more scalable software architecture for on-demand and dedicated Web services. These advantages may attract more geospatial scientists to utilize cloud computing for the development of Digital Earth simulation models in the future. 相似文献
3.
云计算作为一种革命性的计算模型住很多行业都成为重要的技术趋势。将云计算应用于空间地理信息领域所形成的空间云计算也逐渐成为整个空间地理信息行业的主流技术。位置服务利用当今高速发展的互联网和多样化的移动终端向用户提供基于位置的信息和娱乐服务。空间信息数据是位置服务平台的重要维成部分。本文首先介绍云计算相关概念,根据对目前位置服务平台存在的问题的剖析提出了基于云计算的位置服务平台建设的思路和平台架构,最后分析了该平台的特点和所形成的产业链。 相似文献
4.
Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? 总被引：1，自引：0，他引：1
《International Journal of Digital Earth》2013,6(4):305-329
Abstract The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC). 相似文献
5.
云计算是目前信息产业最热门的技术之一,GIS厂商纷纷将GIS软件迁移至云计算环境。与国外云计算部署不同,国内更青睐私有云技术,企业、政府机构纷纷搭建私有云GIS平台。针对风电场设计的特点,本文基于目前云计算应用和研究,构建了私有云GIS平台的体系框架,并对平台所涉及的主要关键技术进行了深入探索,对私有云GIS模式下的资源进行了详细的分类,包括核心服务、服务管理、用户访问接口3个部分。其中,核心服务将硬件基础设施、软件运行环境、应用程序抽象成服务,可满足多样化的风电场设计应用需要。 相似文献
6.
云计算在GIS系统模型中的应用 总被引：2，自引：1，他引：1
云计算是基于网络的计算模型,通过构建云计算基础设施来为上层的云端应用提供支撑环境。将GIS与云计算相结合,能够为GIS的信息存储、处理及其应用提供新的发展前景。结合云计算与GIS,提出了基于云计算的GIS系统模型。以云计算的数据存储和透明化用户服务为基础,构建以GIS基础服务设施为服务支撑平台和以GIS基础信息数据与GIS应用程序为应用支撑平台。从而使GIS能够在通过云计算进行底层数据和服务的网络化资源分配,同时为用户提供稳定高效可靠的GIS服务。 相似文献
7.
《International Journal of Digital Earth》2013,6(4):297-312
Abstract Global challenges (such as economy and natural hazards) and technology advancements have triggered international leaders and organizations to rethink geosciences and Digital Earth in the new decade. The next generation visions pose grand challenges for infrastructure, especially computing infrastructure. The gradual establishment of cloud computing as a primary infrastructure provides new capabilities to meet the challenges. This paper reviews research conducted using cloud computing to address geoscience and Digital Earth needs within the context of an integrated Earth system. We also introduce the five papers selected through a rigorous review process as exemplar research in using cloud capabilities to address the challenges. The literature and research demonstrate that spatial cloud computing provides unprecedented new capabilities to enable Digital Earth and geosciences in the twenty-first century in several aspects: (1) virtually unlimited computing power for addressing big data storage, sharing, processing, and knowledge discovering challenges, (2) elastic, flexible, and easy-to-use computing infrastructure to facilitate the building of the next generation geospatial cyberinfrastructure, CyberGIS, CloudGIS, and Digital Earth, (3) seamless integration environment that enables mashing up observation, data, models, problems, and citizens, (4) research opportunities triggered by global challenges that may lead to breakthroughs in relevant fields including infrastructure building, GIScience, computer science, and geosciences, and (5) collaboration supported by cloud computing and across science domains, agencies, countries to collectively address global challenges from policy, management, system engineering, acquisition, and operation aspects. 相似文献
8.
云平台作为云计算服务的基础架构,在计算机网络的基础上提供各种计算资源的统一管理和动态分配。文章提出的基于云平台的遥感信息公共服务,就是借助云平台先进的基础架构和管理方式,构建快速有效的遥感信息公共服务,推进遥感信息的应用和遥感信息事业的进步。文章在引进云平台技术的基础上,分析遥感信息公共服务平台的架构和关键技术,并对服务平台的应用前景和进一步研究进行展望。 相似文献
9.
Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds. 相似文献
10.
云计算面临的最大挑战是安全问题。云计算应用的无边界性、流动性等特点,较之传统的IT模式有很大差异。在云计算环境下,服务方式发生变化,安全的责任主体也发生了根本改变。作为云计算服务提供商,需要建立安全的云计算平台,为云安全服务提供保障;同时,服务一定是开放的、安全的,要保护云用户敏感信息的安全。整合桌面安全管理技术是行之有效的。云安全应用研究主要是从云计算平台系统安全和网络安全设备、安全基础设施的“云化”突破几个方面展开。 相似文献
11.
车联网是当前缓解交通问题的主要技术手段之一。利用云计算技术及面向服务的软件架构思想,从服务差异化、安全机制等方面考虑,完成了基于云计算的车联网信息服务平台的架构设计、功能结构设计、软件设计;在基础地理空间云服务、车联网功能云服务的基础上,构建高性能、可扩展、支持差异化的信息服务平台。为改善城市交通状况提供了一种新的方法。 相似文献
12.
李月华 《测绘与空间地理信息》2017,(1)
针对环保空间信息平台用户的需求,设计实现了基于WebGIS的环保监管云系统平台。该平台的建设以贵州省委、省政府制定的大数据产业战略,面向贵州经济社会跨越式发展的需求,以贵州省"环境监管"云平台为核心内容,综合运用大数据技术、并行云计算技术、WebGIS技术和高效网络传输技术等当前先进的信息化技术手段,整合和迁移各类地理信息资源和环境保护业务资源,建立统一的环境信息资源数据库,统一的地理信息数据应用标准规范,建成依托贵州省基础地理空间数据库,结合贵州省环境数据中心及其他环境业务信息系统,创新构建全国领先的环境保护工作,实现政府监管、企业自律、公众参与的社会管理"贵州模式",为贵州省环境管理工作提供强大且完备的技术支撑。 相似文献
13.
在地理空间框架升级为时空信息云平台过程中,不仅面临着信息量、支撑技术和应用模式的提档升级,还面临着数据组织模式、管理模式以及应用模式等方面的挑战。在综合考虑人类认知、云计算特征以及地理空间框架现状的基础上,本文提出了时空实体对象化模型,依托行为与事件,实现几何、属性以及时态的一体化建模,并探讨了以地名地址为实体单元的地理空间框架实体化重构步骤,以期更好地满足云环境下的存储、管理以及服务要求。 相似文献
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传统的单机环境和封闭式网络环境由于有限的资源利用能力, 难以充分支持分散地学数据、模型等资源的共享与应用集成。基于网络环境的信息交换特点, 提出了分布式地理空间模型共享的服务体系。该体系以数据、模型、元数据等互操作要素为核心, 通过网络将数据、模型等网络节点进行开放式耦合。针对地理空间模型服务的互操作问题, 提出了分布式环境下的模型共享服务交互接口, 该接口定义了模型服务元数据、模型服务的交互操作、模型服务的通讯方式等交互规则, 尽可能地降低模型服务与模型终端之间在数据交换、功能调用等方面的互操作困难。为了降低将模型共享为模型服务的实现难度, 设计和开发了地理空间模型共享平台, 并介绍了在该平台上发布地理空间模型的2种方法。最后介绍了研究成果在Prairie生态模型共享方面的应用实践。 相似文献
16.
Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package ( http://code.google.com/p/eo4vistrails/ ). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC. 相似文献
17.
云计算是一种新型的分布式计算模式,也是当前IT界研究热点。云GIS平台提供了一种稳定、高效、低成本、环保的支撑应用架构,可以构建各种基于Web的弹性的、按需的地理信息服务,必将成为下一代GIS服务平台。本文在分析了国内外云GIS平台发展现状的基础上,研究了涉及云GIS平台构建的基础设施虚拟化、地理数据云存储、GIS自动化部署等关键技术,从而为相关云GIS平台建设提供一定的指导与借鉴。 相似文献
18.
《International Journal of Digital Earth》2013,6(4):404-425
Abstract The emergence of Cloud Computing technologies brings a new information infrastructure to users. Providing geoprocessing functions in Cloud Computing platforms can bring scalable, on-demand, and cost–effective geoprocessing services to geospatial users. This paper provides a comparative analysis of geoprocessing in Cloud Computing platforms – Microsoft Windows Azure and Google App Engine. The analysis compares differences in the data storage, architecture model, and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms; emphasizes the importance of virtualization; recommends applications of hybrid geoprocessing Clouds, and suggests an interoperable solution on geoprocessing Cloud services. The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern, once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies. The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms. The tested services are developed using geoprocessing algorithms from different vendors, GeoSurf and Java Topology Suite. The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services. 相似文献
19.
GIS系统因其与生俱来的空间属性,正面临着数据密集、计算密集、高并发、海量时空数据运算等挑战.云计算具有资源池化、弹性伸缩、按需使用等优势,可实现更高性能、更高并发的应用,从而协助解决GIS面临的问题.本文提出了基于云端一体化技术体系的GIS系统架构,包括集约化的GIS云、多样化的GIS端,以及云端互联的GIS系统三个部分.集约化的GIS云平台集成了高性能跨平台、智能集群、并行切图、并行空间分析等技术,可集约利用云计算资源,并提供高效的GIS服务.多样化的GIS端集成桌面、Web移动端技术,可构建跨多端设备的GIS应用.云端互联的一体化系统通过云端一体化、云端协同技术,实现云端之间高效互联、协同工作的GIS应用模式.云端一体化GIS系统致力于让GIS充分利用云计算的优势,应对大数据时代的数据密级、计算密集等挑战. 相似文献
20.
《International Journal of Digital Earth》2013,6(1):13-53
ABSTRACTBig Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications. 相似文献