Аннотации:
The rapid increase in the size, volume, diversity and speed of
geospatial data leads to the availability of spatial data infrastructures and compatible
services, As a result we face the necessity of the knowledge extraction from bulk
data. The production of knowledge by different intellectual analysis methods of
distributed data in the spatial database remains a critical issue.
The article presents the most common technologies of data mining. The
authors review the process of knowledge discovery in databases and consider the
data mining technologies as one of the component of this process.
In the article we analyse the technologies of data mining as the basis of web
mining technology. The categories of Web Mining: analysis of the use of web
resources; extraction of web structures; web content extraction. The authors offer to
examine the main mechanisms of research and extraction of information from webdocuments
and services.
In the article we substain the using of the web services of knowledge
production as a layer over spatial data infrastructures as an effective methods to
provide users with spatial data and decision making, the ability to extract knowledge
from arrays of heterogeneous spatial data. One of the main emphasis of the article is the that the Hadoop and Spark
frameworks provide high productivity of extracting templates and knowledge from
large amounts of spatial data. Web services for extracting knowledge from real
geodata allow us to use a dynamic, simpler and much faster procedure for producing
knowledge from spatial data.
