How to quantify the impact of lossy transformations on change detection
How to quantify the impact of lossy transformations on change detection
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DOI：
10.1145/2791347.2791371
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发表时间：
2015-06
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影响因子：
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通讯作者：
Pavel Efros;Erik Buchmann;Adrian Englhardt;Klemens Böhm
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文献类型：
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作者：
Pavel Efros;Erik Buchmann;Adrian Englhardt;Klemens Böhm
为了缓解大数据的扩散，它经常被转换，无论是通过压缩还是匿名化。然而，这样的转换修改了数据的特征，例如时间序列的变化。然而，这些变化对后续分析很重要。这些修改的影响取决于应用程序场景，量化它远非微不足道。这是因为转换可以转移或修改现有的更改或引入新的更改。在本文中，我们提出了米尔顿，一个灵活和强大的措施，量化的影响有损变换后续的变化detectionON。米尔顿适用于时间序列数据的任何有损变换技术和任何通用的变化检测方法。我们已经用三个真实世界的用例对其进行了评估。我们的评估表明，米尔顿允许量化有损变换的影响，并选择最好的一类变换技术为给定的应用场景。
To ease the proliferation of big data, it frequently is transformed, be it by compression, be it by anonymization. Such transformations however modify characteristics of the data, such as changes in the case of time series. Changes however are important for subsequent analyses. The impact of those modifications depends on the application scenario, and quantifying it is far from trivial. This is because a transformation can shift or modify existing changes or introduce new ones. In this paper, we propose MILTON, a flexible and robust Measure for quantifying the Impact of Lossy Transformations on subsequent change detectiON. MILTON is applicable to any lossy transformation technique on time-series data and to any general-purpose change-detection approach. We have evaluated it with three real-world use cases. Our evaluation shows that MILTON allows to quantify the impact of lossy transformations and to choose the best one from a class of transformation techniques for a given application scenario.