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Efficient String Sort with Multi-Character Encoding and Adaptive Sampling

Summary: Proposes a multi-character encoding to shrink radix in string sorting, leveraging sparse alphabet use and distinguishing-prefix sparsity. Paired with adaptive sampling, it blends radix sort with sample sort, delivering ~4.85x STL, ~1.5x state-of-the-art radix, and competitive parallel performance (pS5). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6271
Venue
SIGMOD
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,679 | 19.88%
DOI
10.1145/3448016.3457319

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Authors

BibTeX Citation

@inproceedings{jin_sigmod21,
        title = {{Efficient String Sort with Multi-Character Encoding and Adaptive Sampling}},
        author = {Jin, Wen and Qian, Weining and Zhou, Aoying},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457319},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457319},
        year = {2021}
}

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

Rank Cited Paper Year Venue Pagerank
3,865 The Case for a Learned Sorting Algorithm 2020 SIGMOD 7.0621718e-05
4,021 PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort 2015 VLDB 6.9501893e-05
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