ASAP: Prioritizing Attention via Time Series Smoothing
Kexin Rong, Peter Bailis
TL;DR
This paper introduces ASAP, a novel adaptive smoothing technique for streaming time series visualization that enhances trend visibility and user accuracy while significantly reducing response times.
Contribution
ASAP is the first method to automatically optimize smoothing in streaming time series visualizations, balancing noise reduction and trend retention with an efficient search strategy.
Findings
Improves user accuracy in detecting deviations by up to 38.4%.
Reduces response times by up to 44.3%.
Operates several orders of magnitude faster than alternatives.
Abstract
Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics…
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