地球信息科学学报

地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (2): 273-281.doi: 10.3724/SP.J.1047.2014.00273

• 地理信息系统平台设计与开发 • 上一篇    下一篇

地理知识云GeoKSCloud：动因、设计开发与应用

吴小竹1,2, 陈崇成1, 林剑峰1, 巫建伟1, 林甲祥1, 雷德龙1, 蔡志明1   

  1. 1. 福州大学福建省空间信息工程研究中心 空间数据挖掘与信息共享教育部重点实验室, 福州 350002;
    2. 福州大学数学与计算机科学学院, 福州 350002
  • 收稿日期:2013-05-15 修回日期:2013-09-23 出版日期:2014-03-10 发布日期:2014-03-10
  • 作者简介:吴小竹（1979- ），男，福建漳州人，讲师，博士生，研究方向为智能信息处理、地理知识服务。E-mail：wxz@fzu.edu.cn
  • 基金资助:

    国家科技支撑计划项目（2013BAH28F00）；福建省科技计划项目（2010I0008、2010HZ0004-1）；欧盟第七框架国际合作项目（FP7-2009-People-IRSES、No.247608）。

GeoKSCloud：Motivation, Design and Application

WU Xiaozhu1,2, CHEN Chongcheng1, LIN Jianfeng1, WU Jianwei1, LIN Jiaxiang1, LEI Delong1, CAI Zhiming1   

  1. 1. Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Centre of Fujian, Fuzhou University, Fuzhou 350002, China;
    2. College of Computer Science and Mathematics, Fuzhou University, Fuzhou 35002, China
  • Received:2013-05-15 Revised:2013-09-23 Online:2014-03-10 Published:2014-03-10
  • Contact: 10.3724/SP.J.1047.2014.00273

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摘要：

有效地发现和利用分布存储、运行的各类空间数据、空间决策分析模型和知识发现算法，已成为当前空间信息处理、知识发现与共享领域最具挑战性的前沿课题之一。首先，本文论述了空间信息处理、知识发现的关键问题、发展现状和趋势。然后，描述了地理知识云的概念特征，提出了地理知识云（GeoKSCloud）的具体实现。该平台构造了可伸缩的空间数据和知识服务存储、运行环境；平台从业务功能上划分为数据聚合中心、知识服务中心、地学问题求解中心、平台控制中心和知识云门户等5大核心模块。其为地学问题求解全过程提供了空间数据集成，知识服务发布、注册、搜索、发现、组合等功能，以及地学问题智能推理和结果可视化表达等工具。本文对海量空间数据云存储与管理、知识云服务管理与组合、地学问题智能求解等平台关键技术进行了论述。最后，本文以历史地震影响场分析为例，分析了平台各组件在问题求解中的交互过程，实例表明，该平台可实现多节点、跨平台、异构地理知识服务的协同式计算，有效地降低地学问题求解的成本和复杂度。

关键词: 地理知识云, 地学问题求解, 云计算

Abstract:

Currently, it is one of the most challenging issues to discover and organize diverse distributed geospatial services for geosciences problem resolving, knowledge innovation and sharing. These services include geospatial data services, geospatial analysis services and geospatial data mining services, etc. Facing this challenge and considering the key points of geospatial information processing, knowledge discovery and sharing, in this paper, the concept of geospatial knowledge cloud is depicted, and a novel cloud-based geographical knowledge service platform named as GeoKSCloud is proposed. Based on cloud computing technology, GeoKSCloud tries to create a unified framework to aggregate a broad variety cross-node and cross-platform geospatial services for end-users. With aim to deal with the compute-intensive and data-intensive challenge of geospatial data processing, the platform adopts the idea of virtualization to construct a scalable computation environment. Five main components of data aggregation, service management, geosciences problem solving, platform control and portal are designed to provide functions of services registry, discovery, composition, execution and data integration. Moreover, supported by natural language understanding, ontology and data visualization technology, the platform offers intelligent reasoning and visualization tools to help users to perform problem solving task more efficiently. The key technologies associated with platform realization are discussed, which includes massive geospatial data cloud storage and management technology, knowledge service management and composition technology, and intelligent geospatial problem solving technology, et al. Finally, a use case of historical seismic influence field analysis is proposed to demonstrate the interoperation of platform components, and the representative user interfaces of platform are illustrated. The case study reveals that GeoKSCloud could reduce the complexity and overhead of geosciences problem solving by coordinating multiple distributed and heterogeneous services.

Key words: cloud computing, geospatial knowledge service, geosciences problem solving