A Theoretical Framework for Distribution-Aware Dataset Search
Summary: Distribution-aware dataset search via percentile (Ptile) and preference (Pref) indexing for centralized and federated settings. Presents lower bounds against near-linear-space in the centralized case and approximate O~(N)-space structures with O~(N) preprocessing and O~(1+OUT) queries, epsilon+2delta accuracy. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Aryan Esmailpour (University of Illinois Chicago)
- 2. Sainyam Galhotra (Cornell University)
- 3. Rahul Raychaudhury (Duke University)
- 4. Stavros Sintos (University of Illinois Chicago)
BibTeX Citation
@inproceedings{esmailpour_pods25,
address = {New York, NY, USA},
series = {{PODS} '25},
title = {{A Theoretical Framework for Distribution-Aware Dataset Search}},
url = {https://dl.acm.org/doi/10.1145/3725227},
doi = {10.1145/3725227},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Esmailpour, Aryan and Galhotra, Sainyam and Raychaudhury, Rahul and Sintos, Stavros},
year = {2025}
}
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