Range Thresholding on Streams

Miao Qiao, Junhao Gan, Yufei Tao
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引用次数: 5

Abstract

This paper studies a type of continuous queries called range thresholding on streams (RTS). Imagine the stream as an unbounded sequence of elements each of which is a real value. A query registers an interval, and must be notified as soon as a certain number of incoming elements fall into the interval. The system needs to support multiple queries simultaneously, and aims to minimize the space consumption and computation time. Currently, all the solutions to this problem entail quadratic time O(nm) to process n stream elements and m queries, which severely limits their applicability to only a small number of queries. We propose the first algorithm that breaks the quadratic barrier, by reducing the computation cost dramatically to O(n + m), subject only to a polylogarithmic factor. The algorithm is general enough to guarantee the same on weighted versions of the queries even in d-dimensional space of any constant d. Its vast advantage over the previous methods in practical environments has been confirmed through extensive experimentation.
流的范围阈值
本文研究了一种称为流上范围阈值(RTS)的连续查询。将流想象为一个无界的元素序列,其中每个元素都是实值。查询注册一个间隔,并且必须在一定数量的传入元素落入该间隔时立即通知查询。系统需要同时支持多个查询,并以最小化空间消耗和计算时间为目标。目前,该问题的所有解决方案都需要O(nm)的二次时间来处理n个流元素和m个查询,这严重限制了它们仅适用于少量查询。我们提出了第一个打破二次障碍的算法,通过将计算成本大幅降低到O(n + m),仅受多对数因素的影响。该算法具有足够的通用性,即使在任意常数d的d维空间中,也能保证查询的加权版本相同。在实际环境中,它比以前的方法具有巨大的优势,这已经通过大量的实验得到了证实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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