Abstract
In recent years, Skyline query based on a multi-dimensional space has become a hot topic in the research of database technology according to its potential applications in data mining and visualization of databases. A variety of high-efficient Skyline query approaches is proposed, such as BNL (Blocked Nested Loop), NN (Nearest Neighbour) and BBS (Branch and Bound Skyline). However, these methods always deal with exact values of properties of objects to get the results (the set of points satisfying the user’s needs exactly), which can’t be carried out with fuzzy information. Also high-performance can’t be obtained with the increasing amounts and dimensions of knowledge. In order to solve this problem, this paper proposes the Skyline adaptive fuzzy query method based on the structure of R-trees and the BBS algorithm. It implements a fuzzy inference, and generates rapidly the possibility of getting appropriate results. Finally, in order to improve the accuracy of the reasoning process, Genetic Algorithms are used to study fuzzy rules automatically.
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Yan, W., Zanni-Merk, C., Rousselot, F. (2011). Skyline Adaptive Fuzzy Query. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_35
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DOI: https://doi.org/10.1007/978-3-642-23863-5_35
Publisher Name: Springer, Berlin, Heidelberg
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