Author:
Fadila Bentayeb
Affiliation:
ERIC, University of Lyon Lumière Lyon2, France
Keyword(s):
OLAP, data warehouse, schema evolution, clustering, k-means, analysis level, dimension hierarchy.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Actual data warehouses models usually consider OLAP dimensions as static entities. However, in practice, structural changes of dimensions schema are often necessary to adapt the multidimensional database to changing requirements. This paper presents a new structural update operator for OLAP dimensions, named
Rollup-WithKmeans based on k-means clustering method. This operator allows to create a new level to which, a pre-existent level in an OLAP dimension hierarchy rolls up. To define the domain of the new level and the aggregation function from an existing level to the new level, our operator classifies all instances of an existing level into k clusters with the k-means clustering algorithm. To choose features for k-means clustering, we pro- pose two solutions. The first solution uses descriptors of the pre-existent level in its dimension table while the second one proposes to describe the new level by measures attributes in the fact table. Moreover, we carried out some experimenta
tions within Oracle 10 g DBMS which validated the relevance of our approach.
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