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Finding Informative Skyline Results Over Incomplete Data With Threshold-Based Bucket Skyline Algorithm
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DOI：10.1109/ACCESS.2025.3648053.png)
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Abstract
En 中文
Skyline queries which return objects that are not dominated by any other objects, have been integrated into various real-world database applications. However, the presence of incomplete data in databases, may lead into cyclic dominance relation that none of the objects are considered as skylines, while the transitive dominance property may no longer hold. Although, these issues have received great attention, most of the existing skyline algorithms typically ignore the quality of the returned skyline results, thereby limiting the insights they offer. In this paper, a Threshold-based Bucket Skyline Algorithm ( $TBSA$ ) is proposed with the aim to derive informative skyline results, i.e. results that are not too few and with low missing rate. $TBSA$ organises the objects in the database into a two-layer structure, namely: bucket and cluster, to ensure only dominant objects are retained for further skyline analysis. Meanwhile, to minimise the tuning iterations, a threshold prediction model is constructed that identifies in advanced the subspaces to be excluded from the skyline computation. Extensive experiments have been carried out on the synthetic and real datasets and the results show that $TBSA$ outperforms the previous approach with respect to the number of iterations, pairwise comparisons, skyline results, informative skyline results, and processing time.
Keywords:
Informative
skyline query
incomplete data
threshold prediction
bucket skyline algorithm
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