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)
Incoming Non-self Citations Over Time
Authors
- 1. Charu C. Aggarwal (IBM)
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 |
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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.
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