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- W4313487624 abstract "Retrieving relevant information in Big Data is a difficult task. Many approaches are used to select highly relevant information. One of them is the Skyline operator, which is used to select the best records in multidimensional data. The computation of the Skyline in large data or Big Data environments is highly expensive. Furthermore, updating the data requires a recomputation of the Skyline which leads to an exponential increase in the computation time. In this paper, we proposed a system that significantly reduces the computation time of the Skyline after updating the data. Our system uses the Skyline computation history to compute the new Skyline after the data is updated. We used in this system our proposed data representation and algorithms to handle the Skyline queries and the update queries. Based on the execution time, the experimental results show that the proposed system gives better results compared to existing approaches by keeping the Skyline Up to date." @default.
- W4313487624 created "2023-01-06" @default.
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- W4313487624 date "2023-03-01" @default.
- W4313487624 modified "2023-09-27" @default.
- W4313487624 title "Skyline recomputation in Big Data" @default.
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- W4313487624 doi "https://doi.org/10.1016/j.is.2022.102164" @default.
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