Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
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
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
RoK: Roll-Up with the K-Means Clustering Method for Recommending OLAP Queries
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
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
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Semantics and usage statistics for multi-dimensional query expansion
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Performing groupization in data warehouses: which discriminating criterion to select?
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
Feature-based recommendation framework on OLAP
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
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An OLAP analysis session can be defined as an interactive session during which a user launches queries to navigate within a cube. Very often choosing which part of the cube to navigate further, and thus designing the forthcoming query, is a difficult task. In this paper, we propose to use what the OLAP users did during their former exploration of the cube as a basis for recommending OLAP queries to the user. We present a generic framework that allows to recommend OLAP queries based on the OLAP server query log. This framework is generic in the sense that changing its parameters changes the way the recommendations are computed. We show how to use this framework for recommending simple MDX queries and we provide some experimental results to validate our approach.