Information storage and retrieval
Information storage and retrieval
Experience with personalization of Yahoo!
Communications of the ACM
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On Schema Evolution in Multidimensional Databases
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Maintaining Data Cubes under Dimension Updates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Ontology Based Personalized Search
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
A new OLAP aggregation based on the AHC technique
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
Efficient and non-parametric reasoning over user preferences
User Modeling and User-Adapted Interaction
OLAP preferences: a research agenda
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
A user-driven data warehouse evolution approach for concurrent personalized analysis needs
Integrated Computer-Aided Engineering
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Multi-dimensional navigation modeling using BI analysis graphs
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
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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.