Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Journal of Intelligent Information Systems
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Intensional Answers to Database Queries
IEEE Transactions on Knowledge and Data Engineering
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
An Information-Theoretic Study on Aggregate Responses
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Using Datacube Aggregates for Approximate Querying and Deviation Detection
IEEE Transactions on Knowledge and Data Engineering
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
A probabilistic model for data cube compression and query approximation
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Compression and Aggregation for Logistic Regression Analysis in Data Cubes
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
myOLAP: An Approach to Express and Evaluate OLAP Preferences
IEEE Transactions on Knowledge and Data Engineering
Mining triadic association rules from ternary relations
ICFCA'11 Proceedings of the 9th international conference on Formal concept analysis
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Proceedings of the 21st ACM international conference on Information and knowledge management
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One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing a concise description of extensional answers (i.e., the sets of retrieved facts), generally relying on knowledge like integrity constraints, taxonomies, or patterns discovered from data. This paper proposes a framework for computing an intensional answer to an OLAP query by leveraging on the previous queries in the current session. Such intensional answer is concise and semantically rich, and allows the size of the extensional answers returned to be reduced, so as to achieve an effective trade-off between conciseness and informational content. After describing the general framework, we propose a specific instantiation that relies on previous contributions in cube modeling and intensional query answering.