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Indexing for Keyword Search with Structured Constraints

Summary: Presents index designs with provable, often near‑optimal guarantees (under standard hardness conjectures) for keyword search combined with structured constraints (range, linear predicates, spatial prioritization). Closes a theory gap where prior solutions were mainly heuristic or naive scans. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1914
Venue
PODS
Year
2023
Pagerank
5.2634238e-05
Overall Rank
9,472 | 35.02%
DOI
10.1145/3584372.3588663

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{lu_pods23,
        address = {New York, NY, USA},
        series = {{PODS} '23},
        title = {{Indexing for Keyword Search with Structured Constraints}},
        url = {https://dl.acm.org/doi/10.1145/3584372.3588663},
        doi = {10.1145/3584372.3588663},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Lu, Shangqi and Tao, Yufei},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,638 A Theoretical Framework for Distribution-Aware Dataset Search 2025 PODS 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

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