Abstract
Skyline and Top-K operators are both multi-criteria preference queries. The advantage of one is a limitation of the other: Top-k requires a scoring function while skyline does not, and Top-k output size is exactly K objects while skyline’s output can be the whole dataset. To cope with this state of affairs, regret minimization sets (RMS) whose output is bounded by K and where there is no need to provide a scoring function has been proposed in the literature. However, the computation of RMS on top of the whole dataset is time-consuming. Hence previous work proposed the Skyline set as a candidate set. While it guarantees the same output, it becomes of no benefit when it reaches the size of the whole dataset, e.g., with anticorrelated datasets and high dimensionality. In this paper we investigate the speedup provided by other skyline related candidate sets computed through the structure Negative SkyCube (NSC) such as Top k frequent skylines. We show that this query provides good candidate set for RMS algorithms. Moreover it can be used as an alternative to RMS algorithms as it provides interesting regret ratio.
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Alami, K., Maabout, S. (2021). Using Multidimensional Skylines for Regret Minimization. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_23
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DOI: https://doi.org/10.1007/978-3-030-78428-7_23
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