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Knowledge discovery standards
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AI
DOI：10.1007/s10462-008-9067-4.png)
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Abstract
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As knowledge discovery (KD) matures and enters the mainstream, there is an onus on the technology developers to provide the technology in a deployable, embeddable form. This transition from a stand-alone technology, in the control of the knowledgeable few, to a widely accessible and usable technology will require the development of standards. These standards need to be designed to address various aspects of KD ranging from the actual process of applying the technology in a business environment, so as to make the process more transparent and repeatable, through to the representation of knowledge generated and the support for application developers. The large variety of data and model formats that researchers and practitioners have to deal with and the lack of procedural support in KD have prompted a number of standardization efforts in recent years, led by industry and supported by the KD community at large. This paper provides an overview of the most prominent of these standards and highlights how they relate to each other using some example applications of these standards.
Keywords:
knowledge discovery
data mining
standards
CRISP-DM
PMML
JDM
OLE-DB
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