Voluminous, time-series data streams originating in continuous sensing environments pose data ingestion and processing challenges. We present a holistic methodology centered around data sketching to address both challenges. We introduce an order-preserving sketching algorithm that we have designed for space-efficient representation of multi-feature streams with native support for stream processing related operations. Observational streams are preprocessed at the edges of the network generating sketched streams to reduce data transfer costs and energy consumption. Ingested sketched streams are then processed using sketch-aware extensions to existing stream processing APIs delivering improved performance. Our benchmarks with real-world datasets show up to a $\sim 8\times$ reduction in data volumes transferred and a $\sim 27\times$ improvement in throughput.

中文翻译：

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Pebbles：利用草图处理大量高速数据流

源于连续感测环境的大量时间序列数据流带来了数据摄取和处理方面的挑战。我们提出了一种以数据草绘为中心的整体方法，以应对这两个挑战。我们介绍了一种保留顺序的草图绘制算法，该算法是为多功能流的空间高效表示而设计的，并为流处理相关操作提供了本机支持。观测流在边缘网络生成草绘的流以减少数据传输成本和能耗。然后使用对现有流处理API的草图感知扩展来处理摄取的草图流，从而提高性能。我们的基准测试与实际数据集一起显示了$ \ sim 8 \ times $ 减少了传输的数据量，并且 $ \ sim 27 \ times $ 吞吐量的提高。