Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure
Summary: Progressive approximate aggregates in multi-dimensional data via a Multi-Resolution Aggregate (MRA) tree; selective traversal yields 100% confidence intervals for SUM, COUNT, MIN, MAX, AVG. Iterative refinement until error or time bound; exact answers also produced by the same data structure with practical speed. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Iosif Lazaridis (University of California Irvine)
- 2. Sharad Mehrotra (University of California Irvine)
BibTeX Citation
@inproceedings{lazaridis_sigmod01,
title = {{Progressive Approximate Aggregate Queries with a Multi-Resolution Tree Structure}},
author = {Lazaridis, Iosif and Mehrotra, Sharad},
series = {{SIGMOD} '01},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/375663.375718},
url = {https://dl.acm.org/doi/10.1145/375663.375718},
year = {2001}
}
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