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Fast Approximate Correlation for Massive Time-series Data

Summary: Fast all-pair Pearson correlation over massive time-series using DFT and graph partitioning to cut I/O and CPU. Two approximation methods with guarantees: bounded-error similarity and thresholding with no false positives/negatives, plus batch caching; up to 17× faster than prior exact methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4304
Venue
SIGMOD
Year
2010
Pagerank
9.8120315e-05
Overall Rank
1,764 | 87.90%
DOI
10.1145/1807167.1807188

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{mueen_sigmod10,
        title = {{Fast Approximate Correlation for Massive Time-series Data}},
        author = {Mueen, Abdullah and Nath, Suman and Liu, Jie},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807188},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807188},
        year = {2010}
}

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