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k -dominant Skyline query algorithm for dynamic datasets
Frontiers of Computer Science ( IF 4.2 ) Pub Date : 2020-09-29 , DOI: 10.1007/s11704-020-9246-2
Zhiyun Zheng , Ke Ruan , Mengyao Yu , Xingjin Zhang , Ning Wang , Dun Li

At present, most k-dominant Skyline query algorithms are oriented to static datasets, this paper proposes a k-dominant Skyline query algorithm for dynamic datasets. The algorithm is recursive circularly. First, we compute the dominant ability of each object and sort objects in descending order by dominant ability. Then, we maintain an inverted index of the dominant index by k-dominant Skyline point calculation algorithm. When the data changes, it is judged whether the update point will affect the k-dominant Skyline point set. So the k-dominant Skyline point of the new data set is obtained by inserting and deleting algorithm. The proposed algorithm resolves maintenance issue of a frequently updated database by dynamically updating the data sets. The experimental results show that the query algorithm can effectively improve query efficiency.



中文翻译:

动态数据集的k主导Skyline查询算法

目前,大多数以k为主的Skyline查询算法都面向静态数据集,本文针对动态数据集提出了一种以k为主的Skyline查询算法。该算法是循环递归的。首先,我们计算每个对象的支配能力,并按支配能力按降序对对象进行排序。然后,我们通过k主导的Skyline点计算算法来维护主导指数的倒排索引。当数据改变时,判断更新点是否会影响k主导的天际线点集。所以k通过插入和删除算法获得新数据集的主导Skyline点。所提出的算法通过动态更新数据集解决了频繁更新的数据库的维护问题。实验结果表明,该查询算法可以有效提高查询效率。

更新日期:2020-09-29
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