  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 that combines
techniques from stream processing, user interface design, and signal processing
via autocorrelation-based pruning, pixel-aware preaggregation, and on-demand
refresh. We demonstrate that ASAP can improve users' accuracy in identifying
long-term deviations in time series by up to 38.4% while reducing response
times by up to 44.3%. Moreover, ASAP delivers these results several orders of
magnitude faster than alternative search strategies.
