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Query Driven Knowledge Discovery in Multidimensional Data.

Jean-François Boulicaut, Patrick Marcel, Christophe Rigotti: Query Driven Knowledge Discovery in Multidimensional Data. DOLAP 1999: 87-93
@inproceedings{DBLP:conf/dolap/BoulicautMR99,
  author    = {Jean-Fran\c{c}ois Boulicaut and
               Patrick Marcel and
               Christophe Rigotti},
  title     = {Query Driven Knowledge Discovery in Multidimensional Data},
  booktitle = {DOLAP '99, ACM Second International Workshop on Data Warehousing
               and OLAP, November 6, 1999, Kansas City, Missouri, USA, Proceedings},
  publisher = {ACM},
  year      = {1999},
  pages     = {87-93},
  ee        = {db/conf/dolap/BoulicautMR99.html, http://doi.acm.org/10.1145/319757.319796},
  crossref  = {DBLP:conf/dolap/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

We study KDD (Knowledge Discovery in Databases) processes on multidimensional data from a query point of view. Focusing on association rule mining, we consider typical queries to cope with the pre-processing of multidimensional data and the post-processing of the discovered patterns as well. We use a model and a rule-based language stemming from the OLAP multidimensional representation, and demonstrate that such a language fits well for writing KDD queries on multidimensional data. Using an homogeneous data model and our language for expressing queries at every phase of the process appears as a valuable step towards a better understanding of interactivity during the whole process.

Copyright © 1999 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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