Abstract
The skyline operator is a powerful means in multi-criteria decision-making since it retrieves the most interesting objects according to a set of attributes. On the other hand, uncertainty is inherent in many real applications. One of the most powerful approaches used to model uncertainty is the evidence theory. Databases that manage such type of data are called evidential databases. In this paper, we tackle the problem of skyline analysis on evidential databases. We first introduce a skyline model that is appropriate to the evidential data nature. We then develop an efficient algorithm to compute this kind of skyline. Finally, we present a thorough experimental evaluation of our approach.
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Elmi, S., Benouaret, K., Hadjali, A., Bach Tobji, M.A., Ben Yaghlane, B. (2014). Computing Skyline from Evidential Data. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_13
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DOI: https://doi.org/10.1007/978-3-319-11508-5_13
Publisher Name: Springer, Cham
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