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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)

Paper ID
5108
Venue
SIGMOD
Year
2015
Pagerank
5.093636e-05
Overall Rank
12,120 | 16.85%
DOI
10.1145/2723372.2749429

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Authors

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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