Abstract
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].
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Ślęzak, D., Wróblewski, J., Eastwood, V., Synak, P. (2008). Rough Sets in Data Warehousing. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_52
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