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Effective Temporal Dependence Discovery in Time Series Data

Summary: Generalizes cohort analysis to temporal dependence discovery via recurrent cohort analysis for time-series behavior. Introduces recurrent-cohort operators and tailored access methods for single-node and distributed engines; experiments show up to 1e6× speedup over DB-layer and ~100× over Spark SQL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11980
Venue
VLDB
Year
2018
Pagerank
5.093636e-05
Overall Rank
11,959 | 17.96%
DOI
10.14778/3204028.3204033

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Authors

BibTeX Citation

@article{cai_vldb18,
        title = {{Effective Temporal Dependence Discovery in Time Series Data}},
        author = {Cai, Qingchao and Xie, Zhongle and Zhang, Meihui and Chen, Gang and Jagadish, H. V. and Ooi, Beng Chin},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {8},
        pages = {893--905},
        doi = {10.14778/3204028.3204033},
        url = {https://doi.org/10.14778/3204028.3204033},
        year = {2018}
}

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Rank Citing Paper Year Venue Pagerank
13,519 Cohort Analysis with Ease 2018 SIGMOD -
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