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Discovering Longest-lasting Correlation in Sequence Databases

Summary: Discovering the longest-lasting highly correlated subsequences in sequence databases without a predefined query length. Introduces a space-constrained index with intra- and inter-object grouping that bounds correlations for subsequences of similar length and offset, enabling a normalized distance metric and scalable evaluation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10842
Venue
VLDB
Year
2013
Pagerank
5.7766335e-05
Overall Rank
6,780 | 53.49%
DOI
10.14778/2535568.244

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{li_vldb13,
        title = {{Discovering Longest-lasting Correlation in Sequence Databases}},
        author = {Li, Yuhong and U, Leong Hou and Yiu, Man Lung and Gong, Zhiguo},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {14},
        pages = {1666--1677},
        doi = {10.14778/2535568.244},
        url = {https://doi.org/10.14778/2535568.244},
        year = {2013}
}

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