基于空间知识管理的地理空间 知识发现的架构和案例研究. 地理空间知识发现定义： Geospatial knowledge discovery (also known as knowledge discovery from GIS, KDG), is to extract interesting.
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Presentation on theme: "基于空间知识管理的地理空间 知识发现的架构和案例研究. 地理空间知识发现定义： Geospatial knowledge discovery (also known as knowledge discovery from GIS, KDG), is to extract interesting."— Presentation transcript:
地理空间知识发现定义： Geospatial knowledge discovery (also known as knowledge discovery from GIS, KDG), is to extract interesting spatial patterns and characteristics, normal spatial and non- spatial data relationship, and universal data characteristics implied in the database from the spatial database. GIS ： geographic information systems
KDG （地理空间知识发现）与源于知识 库的知识发现之间的不同： Different from the general KDD (Knowledge discovery form database) whose database is conventional transactional （处理） database, KDG is added a spatial dimension (scale) on discovery state space theory by common database
(1) Rich data source, large amounts of data, vague information （模糊 信息）, abundant data types and complex access methods; (2) Organizing data by spatial indexing （索引） mechanism; (3) A wide range of applications, and all data relate to spatial location can be mined （挖掘） ; (4) A mass of mining methods and algorithms, most of which are complex ones; (5) Diverse manners of knowledge representation, and depends on the person's awareness of the objective world on understanding and appreciation of knowledge; (6) Multi-scale high-dimensional of spatial data, and high self- correlation （自相关性） between one another
第二部分 FRAMEWORK OF SPATIAL KNOWLEDGE MANAGEMENT AND SPATIAL KNOWLEDGE MANAGEMENT SYSTEM 空间知识管理和空间知识管理系统架构
知识管理定义： Knowledge management （ KM ） is the process of turning the various sources of information available for organization into knowledge, and linking it with peoples.
知识管理的特点： ① KM provides a new way for enterprises to achieve sharing of explicit and tacit knowledge （实现共 享） ② improve the responsive and innovational ability of the enterprise by the use of collective wisdom （运 用集体智慧提高反应和改革能力）. ③ In addition to generating new knowledge, the motive of KM more importantly is to make a good use of existing knowledge, serve human community, and to make preparations for the promotion of the new knowledge coming into being as well
知识管理系统定义： KMS (Knowledge Management System) refers to the information systems adopted by organizations when knowledge, that is, an information technology-based systems to support and assist organizations of knowledge management activities as the creation, access, transfer, application of knowledge.
知识管理系统特点： KMS can create a knowledge-based corporate culture, cultivate learning institutions and organizations, enhance innovation capabilities and strengthen sharing capabilities of enterprise. It is not a mere knowledge distribution system, but rather an interactive and open work environment.
空间知识定义： Spatial knowledge is the knowledge extracted and mined from spatial information for the user to directly use, and is the mapping about the field of geographic space, reflecting the attributes （属性）, links （关 系）, characteristics and laws of it. All knowledge related to the spatial location belongs to spatial knowledge.
空间知识与一般知识的区别： In addition to geographical distribution and heterogeneous characteristics( 分 布式和异构特征） same as the general knowledge, spatial knowledge possess the characteristics of dynamic, multisource, mass, diverse storage format and complete systematic, etc..
Semantic modeling for the Internet of Things has become fundamental to resolve the problem of interoperability given the distributed （协同分布） and heterogeneous nature （异构类型） of the “Things” Most of the current research has primarily focused on devices and resources modeling while paid less attention on access and utilisation of the information generated by the things. We present the design of a comprehensive description ontology for knowledge representation in the domain of Internet of Things and discuss how it can be used to support tasks such as service discovery （服务发现）, testing （服务测试） and dynamic composition （ 动态构图）.
物联网定义： The Internet of Things (IoT) refers to interconnection of the objects or things in the physical world and their virtual representations on the Internet. Among these developments semantic oriented computing （面向语义的计算） manifests its potential to cope with the challenging problems of heterogeneity and interoperability （异构性和 互操作性） exposed by the large number of things with different characteristics.
前人工作的不足： Semantic modeling for the IoT domain provides a basis for interoperating among different systems and applications; however, current work has mostly focused on IoT resources management while not on how to access and utilise information generated in IoT. 本文的创新性： In this paper, we present a description ontology for the IoT domain by integrating and extending existing work in modeling concepts on the IoT. The ontology helps exploit （开拓） the synergy of the existing efforts and provides support for crucial tasks in such as IoT resource and service discovery, IoT service testing, composition, adaptation and etc. The ontology is compatible with several widely used semantic models in IoT and is designed to be lightweight to promote reuse and support more efficient inference （推理）.
The description ontology is developed using a knowledge- driven approach and aims to capture most of the important concepts and their relationships in the IoT domain. The ontology consists of three main components: ServiceProperty, （服务属性） LocationProperty, （定位 属性） and PhysicalProperty （物理属性）. ServiceProperty explains the functionality of a service, while properties in the other two components describe contextual and physical characteristics of the sensor nodes in wireless sensor network architecture.
(1) Lightweight: （轻质性）： lightweight ontology model that well balances expressiveness and inference complexity is more likely to be widely adopted and reused. (2) Completeness （完整性）： we aim to develop a more complete description ontology for the IoT domain by integrating and extending existing works on IoT modeling. Users of the ontology can exploit the synergy of integration （整合） to support common tasks in IoT. (3) Compatibility （兼容性） : the ontology needs to be consistent with those well designed, existing ontologies to ensure compatibility. (4) Modularity （模块化） : the designed ontology is developed with a highly modular approach to facilitate its evolution, extension and integration with external ontologies
物联网服务定义： IoT services are exposed by IoT resources and mostly provide (near) real-time and transient （瞬时） information on the physical world through standard service interfaces.
1. 物联网服务特点： ① They usually operate in harsh and dynamic environments （复杂、动态） where the resources are constrained （ 被限制） and may appear or vanish suddenly; therefore, IoT services have to include testing from the beginning. ② Compared to the well-engineered high-level business services, IoT services tend to be less reliable because of the nature of IoT resources and their operating environments. This necessitates the need for testing throughout the service lifecycle and additional mechanisms for service adaptation and re-composition （服务匹配和重构） 2. 物联网服务技术： SOAP 、 REST 、 WADL 、 WSDL
QoS and QoI become more essential because high level business services and applications which utilise the IoT data need to monitor them in order to choose more reliable and quality IoT services. Moreover, they serve as important criteria in decision-making for service adaptation and re-composition. （服务匹配和重构 中起到决策作用）
The distributed and heterogeneous nature （分布 式和异构类型） of IoT Services demand for strong Test capabilities: ① to ensure the correct functionalities of the services during design and deployment phases; ② to ensure that the performance of the services meets the users’ requirements as well as service level agreements between service providers and consumers.
总结论文工作： The description ontology we present here integrates the existing efforts for modeling the IoT domain concepts and is extended with essential concepts such Testing to ensure correct functionality of IoT services at both design and runtime stages. It also contains QoS and QoI modelling which is particularly important for IoT based service composition and adaptation. 未来研究方向： Our future work involves development of efficient and QoS and QoI aware methods for service composition and adaptation in dynamic environment.