In recent years, time-series data have emerged in a variety of application domains, such as wireless sensor networks and surveillance systems. To identify the similarity between time-series data, the Euclidean distance and its variations are common metrics that quantify the differences between time-series data. However, the Euclidean distance is limited by its inability to elastically shift with the time axis, which motivates the development of dynamic time warping (DTW) algorithms. While DTW algorithms have been proven very useful in diversified applications like speech recognition, their efficacy might be seriously affected by the resolution of the time-series data. However, high-resolution time-series data might take up a gigantic amount of main memory and storage space, which will slow down the DTW analysis procedure. This makes the upscaling of DTW analysis more challenging, especially for in-memory data analytics platforms with limited nonvolatile memory space. In this paper, we propose a strategy to downsample time-series data to significantly reduce their size without seriously affecting the precision of the results obtained by DTW algorithms (downsizing without downgrading). In other words, this paper proposes a technique to remove the unimportant details that are largely ignored by DTW algorithms. The efficacy of the proposed technique is verified by a series of experimental studies, where the results are quite encouraging.