Abstract
Data Mining component is included Microsoft SQL Server 2000, on of the most popular DBMS. This gives a push for Data Minwg technologies moving from niche towards mainstream. Apart from a few algorithms, the main contribution of SQL Server Data Mining is the implementation of OLE DB for Data Mining. OLE DB for Data mining is an industrial standard lead by Microsoft and supported by a number of ISVs. It leverages from two existing relational technology: SQL and OLE DB. It defines a SQL language for data mining query based on relational concept. More recently, Microsoft, Hyperion, SAS and a few other BI vendors formed the XML for Analysis Council. The XML for Analysis covers both OLAP and Data Mining. The goal is to allow consumer applications to query various BI packages from different platform. This chapter gives an overview of OLE DB for Data Mining and XML for Analysis. It also shows how to build Data Mining application using these APIs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Paul Bradley, Usama Fayyad, Cory Reina, Scaling Expectation Maximization Clustering to Large Databases, Microsoft Tech. Report MSR-TR-98-35, Microsoft, 1998.
Surajit Chaudhuri, Usama Fayyad, Jeff Bernhardt, Scalable Classification over SQL Databases. ICDE 1999, pp. 470–479.
Huan Liu, Hiroshi Motoda (ed.), Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Publishers, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Tang, Z., Maclennan, J., Kim, P.(. (2005). Building Data Mining Solutions With OLE DB for DM and XML for Analysis. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_64
Download citation
DOI: https://doi.org/10.1007/0-387-25465-X_64
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24435-8
Online ISBN: 978-0-387-25465-4
eBook Packages: Computer ScienceComputer Science (R0)