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ASAP: Prioritizing Attention via Time Series Smoothing

Summary: ASAP: smoothing time series to highlight long-term trends. Novel operator optimizes var–kurt with autocorrelation pruning and pixel-aware preaggregation; on-demand refresh yields 38.4% accuracy gains and 44.3% latency reductions, faster than work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11422
Venue
VLDB
Year
2017
Pagerank
6.1952934e-05
Overall Rank
4,418 | 69.30%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
1,354 Northstar: An Interactive Data Science System 2018 VLDB 0.00012424105
6,205 OnlineSTL: Scaling Time Series Decomposition by 100x 2022 VLDB 5.1541127e-05
9,534 TSExplain: Surfacing Evolving Explanations for Time Series 2021 SIGMOD 4.3228164e-05
10,061 Cleaning Time Series under Seasonal and Trend Constraints 2026 SIGMOD 4.1905499e-05
11,733 Vocalizing Large Time Series Efficiently 2018 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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