Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/54566

TitleEffective OLAP mining of evolving data marts
Author(s)Alves, Ronnie
Belo, Orlando
Costa, Fabio
Issue date2007
PublisherIEEE
JournalInternational Database Engineering and Applications Symposium - Proceedings
Abstract(s)Organizations have been used decisions support systems to help them to understand and to predict interesting business opportunities over their huge databases also known as data marts. OLAP tools have been used widely for retrieving information in a summarized way (cube-like) by employing customized cubing methods. The majority of these cubing methods suffer from being just data-driven oriented and not discovery-driven ones. Data marts grow quite fast, so an incremental OLAP mining process is a required and desirable solution for mining evolving cubes. In order to present a solution that covers the previous mentioned issues, we propose a cube-based mining method which can compute an incremental cube, handling concept hierarchy modeling, as well as, incremental mining of multidimensional and multilevel association rules. The evaluation study using real and synthetic datasets demonstrates that our approach is an effective OLAP mining method of evolving data marts.
TypeConference paper
URIhttp://hdl.handle.net/1822/54566
ISBN9780769529479
DOI10.1109/IDEAS.2007.4318096
ISSN1098-8068
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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