Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
How to barter bits for chronons: compression and bandwidth trade offs for database scans
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Sybase IQ multiplex - designed for analytics
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Detecting attribute dependencies from query feedback
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Foundations and Trends in Databases
Handbook of Granular Computing
Handbook of Granular Computing
The Database Architecture Jigsaw Puzzle
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Semantic knowledge integration to support inductive query optimization
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
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The theory of rough sets [15,16], based on the universal framework of information systems, provides a powerful model for representing patterns and dependencies both in databases and in data mining. On the one hand, although there are numerous rough set applications to data mining and knowledge discovery [10,18], the usage of rough sets inside the database engines is still quite an uncharted territory. On the other hand, however, this situation is not so exceptional given that even the most well-known paradigms of machine learning, soft computing, artificial intelligence, and approximate reasoning are still waiting for more recognition in the database research, with huge potential in such areas as, e.g., physical data model tuning or adaptive query optimization [2,3].