Sprintz：物联网的时间序列压缩
Sprintz: Time Series Compression for the Internet of Things
摘要：
由于连接设备的快速增长，传感器生成的时间序列构成了世界数据的一个巨大且不断增长的部分。通常，这些数据是从分布式的、资源受限的设备收集的，并集中在一个或多个服务器上。这种设置的一个关键挑战是在不牺牲质量的情况下减少传输数据的大小。较低的质量降低了数据的效用，但较小的大小可以降低服务器的网络和存储成本，并降低传感设备的功耗。一个自然的解决方案是在传感设备上压缩数据。不幸的是，现有的压缩算法要么违反了这些设备常见的内存和延迟限制，或者如我们实验所示，在传感器生成的时间序列上表现不佳。我们引入了一种时间序列压缩算法，该算法实现了最先进的压缩比，同时需要不到1 KB的内存，并且几乎不增加延迟。这种方法不仅适用于收集数据的低功耗设备，也适用于存储和查询数据的服务器;在后一种情况下，它可以在单线程中以超过3GB/s的速度解压缩，甚至比许多压缩比低得多的算法还要快。我们的方法的一个关键组成部分是一个高速预测算法，可以在线训练，并显着优于替代品，如三角洲编码。对来自许多领域的数据集进行的大量实验表明，这些结果不仅适用于传感器数据，而且适用于各种其他时间序列。
英文摘要：
Thanks to the rapid proliferation of connected devices, sensor-generated time series constitute a large and growing portion of the world's data. Often, this data is collected from distributed, resource-constrained devices and centralized at one or more servers. A key challenge in this setup is reducing the size of the transmitted data without sacrificing its quality. Lower quality reduces the data's utility, but smaller size enables both reduced network and storage costs at the servers and reduced power consumption in sensing devices. A natural solution is to compress the data at the sensing devices. Unfortunately, existing compression algorithms either violate the memory and latency constraints common for these devices or, as we show experimentally, perform poorly on sensor-generated time series. We introduce a time series compression algorithm that achieves state-of-the-art compression ratios while requiring less than 1KB of memory and adding virtually no latency. This method is suitable not only for low-power devices collecting data, but also for servers storing and querying data; in the latter context, it can decompress at over 3GB/s in a single thread, even faster than many algorithms with much lower compression ratios. A key component of our method is a high-speed forecasting algorithm that can be trained online and significantly outperforms alternatives such as delta coding. Extensive experiments on datasets from many domains show that these results hold not only for sensor data but also across a wide array of other time series.
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。
