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Parallel Algorithms for Constructing Range and Nearest-Neighbor Searching Data Structures

Summary: Provably-efficient MPC/MapReduce algorithms to build and query kd-trees, range trees, and BBD-trees for range and approximate nearest-neighbor search. O(1) communication rounds for preprocessing and querying, competitive runtimes/workloads; randomized with deterministic variants at modest extra cost. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1697
Venue
PODS
Year
2016
Pagerank
7.2030591e-05
Overall Rank
3,685 | 74.72%
DOI
10.1145/2902251.2902303

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{agarwal_pods16,
        address = {New York, NY, USA},
        series = {{PODS} '16},
        title = {{Parallel Algorithms for Constructing Range and Nearest-Neighbor Searching Data Structures}},
        url = {https://dl.acm.org/doi/10.1145/2902251.2902303},
        doi = {10.1145/2902251.2902303},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Agarwal, Pankaj K. and Fox, Kyle and Munagala, Kamesh and Nath, Abhinandan},
        year = {2016}
}

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