Fast Time Sequence Indexing for Arbitrary Lp Norms
Fast Time Sequence Indexing for Arbitrary Lp Norms
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发表时间：
2000-09
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通讯作者：
Byoung-Kee Yi;C. Faloutsos
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作者：
Byoung-Kee Yi;C. Faloutsos
在时间序列数据库中进行快速索引以实现相似性检索是近年来的研究热点。然而，大多数提案通常都围绕欧几里得距离及其导数。我们研究的问题，多模态相似性搜索，用户可以选择最好的一个从多个相似性模型为他们的需要。本文提出了一种新的时间序列快速索引方案，当距离函数为任意Lp范数（p = 1; 2;：：：;1）时，该索引方案是一种新的时间序列快速索引方案.所提出的方法的一个特点是，只需要一个索引结构的所有Lp范数，包括流行的欧几里德距离（L2范数）。我们的计划实现了显着的加速比最先进的：广泛的实验真实的和合成的时间序列表明，所提出的方法是最好的竞争对手为L2和L1规范，但显着（高达10倍）更快的L1规范。
Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multimodal similarity search in which users can choose the best one from multiple similarity models for their needs. In this paper, we present a novel and fast indexing scheme for time sequences, when the distance function is any of arbitrary Lp norms (p = 1; 2; : : : ;1). One feature of the proposed method is that only one index structure is needed for all Lp norms including the popular Euclidean distance (L2 norm). Our scheme achieves significant speedups over the state of the art: extensive experiments on real and synthetic time sequences show that the proposed method is comparable to the best competitor forL2 andL1 norms, but significantly (up to 10 times) faster for L1 norm.