Abstract
Querying databases to search for the best objects matching user’s preferences is a fundamental problem in multi-criteria databases. The skyline queries are an important tool for solving such problems. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in Pareto sense) objects from a set of data. However, this process may lead to a huge skyline problem as the size of the results of skyline grows with the number of criteria (dimensions). In this case, the skyline is less informative for the end-users. In this paper, we propose an efficient approach to refine the skyline and reduce its size, using some advanced techniques borrowed from the formal concepts analysis. The basic idea is to build the fuzzy lattice of skyline objects based on the satisfaction rate of concepts. Then, the refined skyline is given by the concept that contains k objects (where k is a user-defined parameter) and has the great satisfaction rate w.r.t. the target concept. Experimental study shows the efficiency and the effectiveness of our approach compared to the naive approach.
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Haddache, M., Hadjali, A., Azzoune, H. (2019). Reducing Skyline Query Results: An Approach Based on Fuzzy Satisfaction of Concepts. In: Cuzzocrea, A., Greco, S., Larsen, H., Saccà, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2019. Lecture Notes in Computer Science(), vol 11529. Springer, Cham. https://doi.org/10.1007/978-3-030-27629-4_19
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