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A Scalable Index for Top-k Subtree Similarity Queries

Summary: Scalable top-k subtree similarity via inverted lists; processes subtrees first and supports incremental updates in linear space. Tuning-free, data-type agnostic; outperforms state-of-the-art indexes in time and memory, up to four orders of magnitude. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5766
Venue
SIGMOD
Year
2019
Pagerank
5.7596335e-05
Overall Rank
6,832 | 53.13%
DOI
10.1145/3299869.3319892

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kocher_sigmod19,
        title = {{A Scalable Index for Top-k Subtree Similarity Queries}},
        author = {Kocher, Daniel and Augsten, Nikolaus},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3319892},
        url = {https://dl.acm.org/doi/10.1145/3299869.3319892},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,502 AS-Parser: Log Parsing Based on Adaptive Segmentation 2023 SIGMOD 6.6599757e-05
8,508 JEDI: These aren't the JSON documents you're looking for... 2022 SIGMOD 5.4123872e-05
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Outgoing Citations (Sorted by Pagerank)

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

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