DBScholar

Back to papers

Hierarchical Subspace Sampling: A Unified Framework for High Dimensional Data Reduction, Selectivity Estimation and Nearest Neighbor Search

Summary: Hierarchical Subspace Sampling unifies data reduction, selectivity estimation, and NN search via locally adaptive subspaces. It reveals subspace structure, scales linearly with size and dimensionality, enabling fast, sampling-based query processing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3433
Venue
SIGMOD
Year
2002
Pagerank
6.0267197e-05
Overall Rank
5,963 | 59.09%
DOI
10.1145/564691.564743

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aggarwal_sigmod02,
        title = {{Hierarchical Subspace Sampling: A Unified Framework for High Dimensional Data Reduction, Selectivity Estimation and Nearest Neighbor Search}},
        author = {Aggarwal, Charu C.},
        series = {{SIGMOD} '02},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/564691.564743},
        url = {https://dl.acm.org/doi/10.1145/564691.564743},
        year = {2002}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
690 Efficient Similarity Search and Classification via Rank Aggregation 2003 SIGMOD 0.0001492934
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers