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Personalized Top-k Skyline Queries In High-dimensional Space

机译:高维空间中的个性化Top-k天际线查询

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摘要

As data of an unprecedented scale are becoming accessible, it becomes more and more important to help each user identify the ideal results of a manageable size. As such a mechanism, skyline queries have recently attracted a lot of attention for its intuitive query formulation. This intuitiveness, however, has a side effect of retrieving too many results, especially for high-dimensional data. This paper is to support personalized skyline queries as identifying "truly interesting" objects based on user-specific preference and retrieval size k. In particular, we abstract personalized skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference. We then develop a novel algorithm navigating on a compressed structure itself, to reduce the storage overhead. Furthermore, we also develop novel techniques to interleave cube construction with navigation for some scenarios without a priori structure. Finally, we extend the proposed techniques for user-specific preferences including equivalence preference. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithms on both real-life and synthetic data.
机译:随着前所未有的规模数据的可访问性,帮助每个用户确定可管理大小的理想结果变得越来越重要。作为这种机制,天际线查询最近以其直观的查询方式引起了很多关注。但是,这种直观性会带来检索过多结果(特别是对于高维数据)的副作用。本文旨在支持个性化天际线查询,以根据用户特定的偏好和检索大小k识别“真正有趣的”对象。特别是,我们将个性化的天际线排名抽象为在用户特定偏好的指导下对天际线子空间进行动态搜索。然后,我们开发了一种在压缩结构本身上导航的新颖算法,以减少存储开销。此外,对于某些没有先验结构的情况,我们还开发了新颖的技术来使多维数据集构造与导航交错。最后,我们将提议的技术扩展到针对用户的偏好,包括等效偏好。我们广泛的评估结果验证了所提出算法在实际数据和综合数据上的有效性和效率。

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