Efficient and Effective KNN Sequence Search with Approximate n-grams
Summary: Proposes KNN sequence search under edit distance using longer approximate n-grams for pruning with a two-level index. CA-based filtering with a frequency queue reduces false positives, enabling progressive results and early termination; parallel execution scales. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoli Wang (National University of Singapore)
- 2. Xiaofeng Ding (Huazhong University of Science and Technology; University of South Australia)
- 3. Anthony K.H. Tung (National University of Singapore)
- 4. Zhenjie Zhang (Advanced Digital Sciences Center)
BibTeX Citation
@article{wang_vldb14,
title = {{Efficient and Effective KNN Sequence Search with Approximate n-grams}},
author = {Wang, Xiaoli and Ding, Xiaofeng and Tung, Anthony K.H. and Zhang, Zhenjie},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {1},
pages = {1--12},
doi = {10.14778/2732232.2732236},
url = {https://doi.org/10.14778/2732232.2732236},
year = {2014}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,726 | Pigeonring: A Principle for Faster Thresholded Similarity Search | 2019 | VLDB | 6.1079184e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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