SMiLer: A Semi-Lazy Time Series Prediction System for Sensors
Summary: SMiLer builds query-dependent GPs on small data per prediction for real-time sensor time series, with no training phase. GPU-based two-level inverted index accelerates DTW-kNN for just-in-time GP construction; adaptive auto-tuning personalizes per-series parameters, improving accuracy and uncertainty estimates. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jingbo Zhou (National University of Singapore)
- 2. Anthony K. H. Tung (National University of Singapore)
BibTeX Citation
@inproceedings{zhou_sigmod15,
title = {{SMiLer: A Semi-Lazy Time Series Prediction System for Sensors}},
author = {Zhou, Jingbo and Tung, Anthony K. H.},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2749429},
url = {https://dl.acm.org/doi/10.1145/2723372.2749429},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,572 | LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index | 2016 | SIGMOD | 0.00010329197 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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