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Reference-Based Indexing of Sequence Databases

Summary: Proposes a reference-based index for large sequence databases under edit distance, reducing expensive computations with limited memory. Two novel reference-selection strategies and a new assignment method prune up to 20x–30x more candidates than Omni and frequency vectors, scalable to long sequences. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9693
Venue
VLDB
Year
2006
Pagerank
5.8632988e-05
Overall Rank
6,496 | 55.44%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{venkateswaran_vldb06,
        title = {{Reference-Based Indexing of Sequence Databases}},
        author = {Venkateswaran, Jayendra and Lachwani, Deepak and Kahveci, Tamer and Jermaine, Christopher},
        journal = {PVLDB},
        series = {{VLDB} '06},
        pages = {906--917},
        year = {2006}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
3,440 Approximate Embedding-Based Subsequence Matching of Time Series 2008 SIGMOD 7.4142918e-05
6,182 Reference-Based Alignment in Large Sequence Databases 2009 VLDB 5.9493566e-05
6,858 A Generic Framework for Efficient and Effective Subsequence Retrieval 2012 VLDB 5.7523396e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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