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}
}

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.

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