Personalization techniques for online recruitment services
Communications of the ACM - The Adaptive Web
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
Maintaining Data Cubes under Dimension Updates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
State-Space Optimization of ETL Workflows
IEEE Transactions on Knowledge and Data Engineering
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
Answering top-k queries with multi-dimensional selections: the ranking cube approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
OLAP preferences: a research agenda
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Recommending Multidimensional Queries
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Preference-Based Recommendations for OLAP Analysis
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
A Conceptual Modeling Approach for OLAP Personalization
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
Evolution of data warehouses' optimization: a workload perspective
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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A perennial challenge faced by many organizations is the management of their increasingly large multidimensional databases (MDB) that can contain millions of data instances. The problem is exacerbated by the diversity of the users' specific needs. Personalization of MDB content according to how well they match user's preferences becomes an effective approach to make the right information available to the right user under the right analysis context. In this paper, we propose a framework called OLAP Content Personalization (OCP) that aims at deriving a personalized content of a MDB based on user preferences. At query time, the system enhances the query with related user preferences in order to simulate its performance upon an individual content. We discuss results of experimentation with a prototype for content personalization.