Abstract
In many recent applications, data are intrinsically uncertain, noisy and error-prone. That is why, uncertain database management has attracted the attention of several researchers. Data uncertainty can be modeled in the evidence theory setting. On the other hand, skyline analysis is a powerful tool in a wide spectrum of real applications involving multi-criteria optimal decision making. It relies on Pareto dominance relationship. However, the skyline maintenance is not an easy task when the queried database is updated. This paper addresses the problem of the maintenance of the skyline objects of frequently updated evidential databases. In particular, we propose algorithms for maintaining evidential skyline in the case of object insertion or deletion. Extensive experiments are conducted to demonstrate the efficiency and scalability of our proposal.
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Elmi, S., Tobji, M.A.B., Hadjali, A., Yaghlane, B.B. (2016). Efficient Skyline Maintenance over Frequently Updated Evidential Databases. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_17
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DOI: https://doi.org/10.1007/978-3-319-40581-0_17
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