Paper Info
Title
RoK: Roll-Up with the K-Means Clustering Method for Recommending OLAP Queries
Abstract
Dimension hierarchies represent a substantial part of the data warehouse model. Indeed they allow decision makers to examine data at different levels of detail with On-Line Analytical Processing (OLAP) operators such as drill-down and roll-up. The granularity levels which compose a dimension hierarchy are usually fixed during the design step of the data warehouse, according to the identified analysis needs of the users. However, in practice, the needs of users may evolve and grow in time. Hence, to take into account the users' analysis evolution into the data warehouse, we propose to integrate personalization techniques within the OLAP process. We propose two kinds of OLAP personalization in the data warehouse: (1) adaptation and (2) recommendation. Adaptation allows users to express their own needs in terms of aggregation rules defined from a child level (existing level) to a parent level (new level). The system will adapt itself by including the new hierarchy level into the data warehouse schema. For recommending new OLAP queries, we provide a new OLAP operator based on the K-means method. Users are asked to choose K-means parameters following their preferences about the obtained clusters which may form a new granularity level in the considered dimension hierarchy. We use the K-means clustering method in order to highlight aggregates semantically richer than those provided by classical OLAP operators. In both adaptation and recommendation techniques, the new data warehouse schema allows new and more elaborated OLAP queries. Our approach for OLAP personalization is implemented within Oracle 10 g as a prototype which allows the creation of new granularity levels in dimension hierachies of the data warehouse. Moreover, we carried out some experiments which validate the relevance of our approach.
Year
DOI
Venue
2009
10.1007/978-3-642-03573-9_43
DEXA
Keywords
Field
DocType
data warehouse model,recommending olap queries,elaborated olap query,data warehouse,dimension hierarchy,olap personalization,classical olap operator,data warehouse schema,olap process,new granularity level,k-means clustering method,new olap operator,decision maker,level of detail,k means,olap,adaptive system,personalization,k means clustering,clustering
Data warehouse,k-means clustering,Data mining,Computer science,Oracle,Hierarchy,Online analytical processing,Cluster analysis,Schema evolution,Database,Personalization
Conference
Volume
ISSN
Citations 
5690
0302-9743
9
PageRank 
References 
Authors
0.52
18
2
Name
Order
Citations
PageRank
Fadila Bentayeb119836.79
Cécile Favre211014.96