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Visualizing and discovering non-trivial patterns in large time series databases

机译：可视化和发现大型时间序列数据库中的非平凡模式

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摘要

Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequently occurring patterns (motif discovery), surprising patterns (anomaly detection), and query by content. VizTree works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic data sets. Based on the tree structure, we further device a coefficient which measures the dissimilarity between any two time series. This coefficient is shown to be competitive with the well-known Euclidean distance.
机译：数据可视化技术对于数据分析非常重要，因为人眼经常被提倡为最终的数据挖掘工具。但是，在可视化大量时间序列数据集方面，几乎没有令人惊讶的工作。为此，我们开发了VizTree，这是一个基于后缀树扩展的时间序列模式发现和可视化系统。 VizTree直观地同时总结了时间序列数据的全局和局部结构。此外，它为许多模式发现问题提供了新颖的交互式解决方案，包括发现频繁发生的模式（主题发现），令人惊讶的模式（异常检测）以及按内容查询。 VizTree的工作原理是将时间序列转换为符号表示形式，并在修改后缀树中对数据进行编码，在后缀树中，模式的频率和其他属性被映射到颜色和其他视觉属性上。通过将其与几种实际和综合数据集上的最新批处理算法进行比较，我们演示了系统的实用性。基于树结构，我们进一步使用一个系数来测量任意两个时间序列之间的差异。该系数显示出与众所周知的欧几里得距离具有竞争力。

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