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首页> 外文会议>High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management >Application of a fast skyline computation algorithm for serendipitous searching problems
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Application of a fast skyline computation algorithm for serendipitous searching problems

机译：快速天际线计算算法在偶然搜索问题中的应用

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摘要

Skyline computation is a method of extracting interesting entries from a large population with multiple attributes. These entries, called skyline or Pareto optimal entries, are known to have extreme characteristics that cannot be found by outlier detection methods. Skyline computation is an important task for characterizing large amounts of data and selecting interesting entries with extreme features. When the population changes dynamically, the task of calculating a sequence of skyline sets is called continuous skyline computation. This task is known to be difficult to perform for the following reasons: (1) information of non-skyline entries must be stored since they may join the skyline in the future; (2) the appearance or disappearance of even a single entry can change the skyline drastically; (3) it is difficult to adopt a geometric acceleration algorithm for skyline computation tasks with high-dimensional datasets. Our new algorithm called jointed rooted-tree (JR-tree) manages entries using a rooted tree structure. JR-tree delays extend the tree to deep levels to accelerate tree construction and traversal. In this study, we presented the difficulties in extracting entries tagged with a rare label in high-dimensional space and the potential of fast skyline computation in low-latency cell identification technology.
机译：天际线计算是一种从大量具有多种属性的人群中提取有趣条目的方法。这些条目被称为“天际线”或“帕累托最优”条目，已知具有异常值检测方法无法找到的极端特征。天际线计算是表征大量数据并选择具有极端特征的有趣条目的重要任务。当种群动态变化时，计算一系列天际线集合的任务称为连续天际线计算。已知由于以下原因而难以执行该任务：（1）必须存储非天际线条目的信息，因为它们将来可能会加入天际线； （2）甚至只有一个入口的出现或消失都会大大改变天际线； （3）对于具有高维数据集的天际线计算任务，很难采用几何加速算法。我们称为联合根树（JR-tree）的新算法使用根树结构管理条目。 JR-tree延迟将树扩展到更深的层次，以加速树的构建和遍历。在这项研究中，我们提出了在高维空间中提取带有稀有标签的条目的困难，以及在低延迟细胞识别技术中快速天际线计算的潜力。

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