何时更新流数据的顺序模式？
When to Update the sequential patterns of stream data?
摘要：
在本文中，我们首先定义了流数据的新旧序列模式之间的差异度量，证明了它是一个距离。在此基础上，提出了一种实验性的方法TPD（Tradeoff between Performance and Difference），该方法通过权衡递增更新算法的性能和序列模式的差异来决定何时更新流数据的序列模式。对增量更新算法IUS在两个数据集上的实验表明，一般情况下，随着增量窗口大小的增大，加速比值减小，差值值增大。实验还表明，由TPD方法确定的增量比不单调增加或减少，但在20和30%之间的范围内变化的IUS算法。
英文摘要：
In this paper, we first define a difference measure between the old and new sequential patterns of stream data, which is proved to be a distance. Then we propose an experimental method, called TPD (Tradeoff between Performance and Difference), to decide when to update the sequential patterns of stream data by making a tradeoff between the performance of increasingly updating algorithms and the difference of sequential patterns. The experiments for the incremental updating algorithm IUS on two data sets show that generally, as the size of incremental windows grows, the values of the speedup and the values of the difference will decrease and increase respectively. It is also shown experimentally that the incremental ratio determined by the TPD method does not monotonically increase or decrease but changes in a range between 20 and 30 percentage for the IUS algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
