Nowadays, extracting knowledge from voluminous and distributed spatial data is a vital need and ultimate goal of spatial data community. In this regard, a number of spatial data mining methods and algorithms have been developed, but there are a number of reasons these methods could not be cover spatial data community requirements. Like early years of GIS development, collecting data on a repository is still applied for knowledge extraction. In adaption, interoperability and usability are two important issues that have been neglected in traditional knowledge extraction methods. To respond the requirements from knowledge perspective a set of Web service-based geographical knowledge extraction is necessary. The services required a technological infrastructure or framework to extract useful knowledge from the different and geographically distributed data sources. In order to overcome this challenges, a general Spatial Knowledge Infrastructure (SKI) proposed to facilitate knowledge extraction in distributed scenarios. The SKI is a widespread interoperable framework through which geographical/spatial knowledge is created, organized, shared, managed and used in many domains. It creates a mechanism to make the necessary processes of geographic knowledge with the highest efficiency and usability. SKI's primary goal is to combine Spatial Data Infrastructure (SDI), Spatial Web Services (SWS) and Spatial Data Mining (SDM) concepts to facilitate geographical knowledge extraction as a collection of services. With a combination of SDI, SWS and SDM concepts this study presents the nature of SKI, including definition, levels, components, and architecture. |
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