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Indexing Metric Uncertain Data for Range Queries

Summary: Introduces object-level and bi-level models for metric uncertain data and two indexes—UPB-tree and UPB-forest—for probabilistic range queries over diverse uncertainty types. Uses pivot-based pruning with probability bounds on a B+-tree backbone, achieving lower construction cost, smaller storage, and faster queries with easy DBMS integration. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5010
Venue
SIGMOD
Year
2015
Pagerank
5.093636e-05
Overall Rank
12,103 | 16.97%
DOI
10.1145/2723372.2723728

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Authors

BibTeX Citation

@inproceedings{chen_sigmod15,
        title = {{Indexing Metric Uncertain Data for Range Queries}},
        author = {Chen, Lu and Gao, Yunjun and Li, Xinhan and Jensen, Christian S. and Chen, Gang and Zheng, Baihua},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2723728},
        url = {https://dl.acm.org/doi/10.1145/2723372.2723728},
        year = {2015}
}

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