Evaluation and Monitoring of Service Quality: Discussing Ways to Meet INSPIRE Requirements

Service oriented architectures (SOA) are widely used nowadays. As the name implies such architectures rely on services. Germany developed a marine‐specific service oriented data infrastructure (MDI‐DE – Marine Dateninfrastruktur Deutschland) from 2010 to 2013. The services in MDI‐DE can contribute to fulfilling reporting commitments for various European and national legislation. The services of MDI‐DE (just like other services affected, for instance, by INSPIRE) have to meet specific requirements regarding performance, availability and conformity (quality of service, QoS). Although SOA is an important field in scientific research there are very few publications and studies available on QoS, especially regarding INSPIRE requirements. The services of MDI‐DE were analyzed using various existing tools. Their usefulness to reflect where either the aspect's performance, availability or conformity needs improvement was partly verified. Due to varying results and the neglect of some services for various reasons it can be stated that the more tools are used, the more conclusive the outcome. Furthermore, service quality was not coherent when measured with different tools. This means that measuring QoS in terms of INSPIRE will be very difficult to do in the future and there is the danger that people will choose the tools with which their services perform best.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

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.     

Normalized evaluation of the performance,capacity and availability of catalogue services: a pilot study based on INfrastruture for SPatial InfoRmation in Europe

Geographic information has a great potential to be re-used when supported by mechanisms for its discovery. Above all, the quality of a catalogue service is the key feature supporting users in the discovery process. So far, there have been in existence various methodologies dealing with the normalized evaluation of quality with respect to catalogue services. Their biggest weakness seems to be in the depth of quality testing, i.e. some influences are beyond the scope of evaluation of these methodologies with respect to quality in catalogue services. In this study, the quality of 45 catalogue services across Europe was verified with the proposed normalized evaluation methodology originating from documents within the INfrastruture for SPatial InfoRmation in Europe (INSPIRE) framework. This paper discusses the (statistical) influence of factors that may significantly change the results of catalogue service testing. The proposals for improving the existing INSPIRE normalized evaluation procedure are applicable for any spatial data infrastructure and/or Digital Earth component using the Open Geospatial Consortium Catalogue Service for the Web as a basis.     

Quality of service management framework for dynamic chaining of geographic information services

Dynamic chaining of geographic information services (geo-services) is gaining popularity as a new paradigm for evolving flexible geo-information systems and for providing on-demand access to geo-information. In dynamic chaining, disparate geo-services are discovered and composed at run time to yield more elaborate functionality and create value-added geo-information. Common approaches to service chaining discover and compose disparate geo-services based on the functional capability of individual geo-services. The primary concern of common approaches is thus the emergent behavior of the resulting composite geo-service. However, as geo-services become mundane and take on a greater and more strategic role in mission critical processes, deliverable quality of service (QoS) becomes an important concern. QoS concerns operational characteristics of a service that determine its utility in an application context. To address pertinent QoS requirements, a new approach to service chaining becomes necessary. In this paper we propose a QoS-aware chaining approach in which geo-services are discovered, composed and executed considering both functional and QoS requirements. We prescribe a QoS management framework that defines fundamental principles, concepts and mechanisms which can be applied to evolve an effective distributed computing platform for QoS-aware chaining of geo-services – the so-called geo-service infrastructure. The paper also defines an extensible QoS model for services delivered by dynamic compositions of geo-services. The process of orthophoto generation is used to demonstrate the applicability of the prescribed framework to service-oriented geographic information processing.     

Semantic Enablement for Spatial Data Infrastructures

Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as a basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo‐semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on‐the‐fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent Semantic Enablement Layer for SDI which also integrates reasoning services known from the Semantic Web. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world. Finally, we point out how to combine SDI with linked data.     

基于网格虚拟组织的空间数据基础设施探讨

网格(Grid)是新型高效的分布式计算和资源管理基础设施。分析了空间数据基础设施(Spatial Data In-frastructure,SDI)与网格技术的集成需求,扩展了传统SDI层次体系,提出一种新的SDI—基于网格虚拟组织的SDI(Virtual Organization SDI,VOSDI)。VOSDI突破原有SDI以明确的地区或地域分类分层组织的方式,通过VO对地理上分布的人员、资料及空间信息进行有效组织和整合,具有灵活高效的特性。对VOSDI的功能、特性进行探讨后,进一步提出VOSDI的概念模型,并指出VOSDI实施中可能存在的问题。     

Geoprocessing in Cloud Computing platforms – a comparative analysis

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.     

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.     

云计算在GIS系统模型中的应用

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。     

风电场设计私有云GIS平台架构研究

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。     

A hybrid approach to modeling territorial control in violent armed conflicts

Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time.     

Moving Code in Spatial Data Infrastructures – Web Service Based Deployment of Geoprocessing Algorithms

This article proposes a concept for offering complex geoprocessing functionality in service‐based Spatial Data Infrastructures (SDI). Today, geoprocessing in SDI is typically realized in a data driven manner. Applying the suggested “moving code” approach in a case study in the field of Spatial Decision Support proves its applicability. The proposed solution is analyzed and assessed in terms of gained efficiency, performance behavior and support for distributed development of geoprocessing functionality. In data and computation intensive SDI applications the deployment of moving code proves to be beneficial.     

雾定位及其应用研究

全球卫星导航系统（GNSS）的发展促进了基于位置服务(LBS)的迅速普及,人们对高可靠、高可信、高精度定位、导航、授时（PNT）服务需求日益迫切．PNT是一个融合多类技术、包括多级系统的体系架构．围绕PNT服务体系优化,国内外学者相继提出了全源导航定位(All source positioning and navigation)、弹性PNT(Resilient PNT)、云定位(Cloud Positioning)等新的架构和技术体系．本文提出以具备通信、计算、存储、定位、感知等能力的异构定位资源为基础设施,通过智能管理与调度分布在不同地理位置的异构定位资源实现用户高可靠、高可信、高精度的PNT信息服务．并由此给出了雾定位(Fog Positioning)与泛源导航定位(Omnipresent Positioning and Navigation)的定义,指出雾定位的定义由分布式计算架构演化而来,强调构成PNT服务体系的架构;而泛源导航定位的概念是从定位技术的发展演化而来,强调利用可获取的泛在导航定位数据源进行协同融合处理,实现泛在定位的能力．在此基础上,通过与云定位比较,指出雾定位是云定位向用户端的延伸,是定位资源的泛在化实现,同时雾是一种动态的、弹性的云,因此雾定位是一种具备“弹性”性能的PNT架构．而泛在定位是PNT信息服务发展重要目标,雾定位给出了实现这一目标的潜在手段,即泛源导航定位．最后,结合城市环境、室内环境等复杂场景,研究了雾定位／泛源导航定位的基本服务模式．      

Advancing Global Marine Biogeography Research with Open‐source GIS Software and Cloud Computing

Across many scientific domains, the ability to aggregate disparate datasets enables more meaningful global analyses. Within marine biology, the Census of Marine Life served as the catalyst for such a global data aggregation effort. Under the Census framework, the Ocean Biogeographic Information System was established to coordinate an unprecedented aggregation of global marine biogeography data. The OBIS data system now contains 31.3 million observations, freely accessible through a geospatial portal. The challenges of storing, querying, disseminating, and mapping a global data collection of this complexity and magnitude are significant. In the face of declining performance and expanding feature requests, a redevelopment of the OBIS data system was undertaken. Following an Open Source philosophy, the OBIS technology stack was rebuilt using PostgreSQL, PostGIS, GeoServer and OpenLayers. This approach has markedly improved the performance and online user experience while maintaining a standards‐compliant and interoperable framework. Due to the distributed nature of the project and increasing needs for storage, scalability and deployment flexibility, the entire hardware and software stack was built on a Cloud Computing environment. The flexibility of the platform, combined with the power of the application stack, enabled rapid re‐development of the OBIS infrastructure, and ensured complete standards‐compliance.     

GPS参考站网络技术

“空间数据基础设施（SDI）”是联合国和美国政府首脑倡导的为迎接新世纪挑战的战略措施，也就是建立起资源、环境、社会、经济等多种信息数据的采集、传输、分享、处理、分析和反馈的服务网络体系。GPS连续运行参考站网络系统是“空间数据基础设施”最重要的组成部分，文中针对GPS参考站建立的有关技术问题进行阐述。     

云定位技术及云定位服务平台

传统的基于行业用户需求而建立的集中式高精度导航定位服务模式在可靠性、可扩展性以及服务多样性等方面已无法满足大众用户的精密定位需求。结合云平台技术,提出了云定位的概念,通过综合管理和整合各类定位资源,实现多种定位手段的资源共享、技术融合和优化配置。给出了云定位的架构图,并讨论了GNSS网络RTK,GNSS广域精密定位,Wi-Fi定位,通信基站定位等多种手段在定位云上的综合和服务实现。云定位在可扩展性、可靠性、系统维护成本以及用户使用灵活性等方面都具有传统的精密定位服务模式所无法比拟的优势;通过云定位,用户不仅可以获取各类精密定位服务,还能实现多种定位资源的优化配置,定制个性化的应用;为精密导航定位的大众化普及提供有效的商业模式和技术途径。     

A Multi‐granularity Parallel Model for Unified Remote Sensing Image Processing WebServices

The growth of the Web has resulted in the Web‐based sharing of distributed geospatial data and computational resources. The Geospatial Processing Web (GeoPW) described here is a set of services that provide a wide array of geo‐processing utilities over the Web and make geo‐processing functionalities easily accessible to users. High‐performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high‐performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi‐granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI‐oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub‐models. In addition, interfaces, definitions, parallel task scheduling and fault‐tolerance mechanisms are described in detail. Based on the model and methods, we propose an open‐source online platform named OpenRS‐Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi‐granularity parallel model for unified remote sensing image processing web services.     

SDI planning using the system dynamics technique within a community of practice: lessons learnt from Tanzania

There exist major challenges in accelerating the spatial data infrastructure (SDI) planning process in the developing countries as well as advocating for politicians to support the development of SDI, due to the high complexity of SDI, lack of knowledge and experience, and limited insight in the benefits. To address these challenges, a methodology for SDI planning in Tanzania, based on the system dynamics technique and the communities of practice concept, was adopted and applied within a community consisting of experts from stakeholder organizations. The groups gathered to develop an SDI plan, while they shared their knowledge and discussed their ideas that helped their understanding of SDI. By running the system dynamics model, the development of SDI over time could be simulated that gave the planning community an insight about the future effects of today’s plans and decisions. Finally, an optimum model could be developed by refinements and improvements done with the consensus of the SDI stakeholders. This model included the components and policies that are essential for a successful SDI implementation in Tanzania and can be used as a basis for SDI planning and help to gain political support. Lessons learnt from this research were promising regarding the usability of the methodology for SDI planning in comparable countries.