Abstract
Tools and techniques used for automatic and smart analysis of huge data repositories of industries, governments, corporations and scientific institutes are the subjects dealt by the field of Knowledge Discovery in Databases (KDD). In MATRIKS context, a framework for KDD, SKDQL (Structured Knowledge Discovery Query Language) is the proposal of a structured language for KDD specification, following SQL patterns within an open and extensible architecture, supporting heterogeneity, interaction and increment of KDD process, with resources for accessing, cleaning, transforming, deriving and mining data, beyond knowledge manipulation.
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© 2004 Springer-Verlag Berlin Heidelberg
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dos Santos Silva, M.P., Robin, J. (2004). SKDQL: A Structured Language to Specify Knowledge Discovery Processes and Queries. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_33
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DOI: https://doi.org/10.1007/978-3-540-28645-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23237-7
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