Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
PROMISE: Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Built-In Indicators to Discover Interesting Drill Paths in a Cube
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Inferring semantic query relations from collective user behavior
Proceedings of the 17th ACM conference on Information and knowledge management
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Query Recommendations for Interactive Database Exploration
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
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
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Built-in indicators to automatically detect interesting cells in a cube
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
CineCubes: cubes as movie stars with little effort
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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Recommending database queries is an emerging and promising field of research and is of particular interest in the domain of OLAP systems, where the user is left with the tedious process of navigating large datacubes. In this paper, the authors present a framework for a recommender system for OLAP users that leverages former users' investigations to enhance discovery-driven analysis. This framework recommends the discoveries detected in former sessions that investigated the same unexpected data as the current session. This task is accomplished by 1 analysing the query log to discover pairs of cells at various levels of detail for which the measure values differ significantly, and 2 analysing a current query to detect if a particular pair of cells for which the measure values differ significantly can be related to what is discovered in the log. This framework is implemented in a system that uses the open source Mondrian server and recommends MDX queries. Preliminary experiments were conducted to assess the quality of the recommendations in terms of precision and recall, as well as the efficiency of their on-line computation.