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Representative Time Series Discovery for Data Exploration

Summary: Defines similarity-bounded representative time series and the min-cardinality cover for a user-specified proportion; proves NP-hard and provides approximation algorithms. Presents a learning-based method that matches effectiveness while achieving up to 21x speedups and 101x memory reduction. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14435
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
11,107 | 23.80%
DOI
10.14778/3712221.3712252

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BibTeX Citation

@article{lee_vldb25,
        title = {{Representative Time Series Discovery for Data Exploration}},
        author = {Lee, Ge and Huang, Shixun and Bao, Zhifeng and Zhao, Yanchang},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {3},
        pages = {915--928},
        doi = {10.14778/3712221.3712252},
        url = {https://doi.org/10.14778/3712221.3712252},
        year = {2025}
}

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