I/O-Conscious Data Preparation for Large-Scale Web Search Engines
Summary: I/O-conscious data preparation for external-memory data in large-scale web search engines using coordinated partitioning and delayed batched execution to maximize temporal locality and sequential I/O. Prototype Yuntis shows ~100x speedups over naïve implementations and scales with distributed storage and parallel processing. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Maxim Lifantsev (State University of New York at Stony Brook)
- 2. Tzi-cker Chiueh (State University of New York at Stony Brook)
BibTeX Citation
@article{lifantsev_vldb02,
title = {{I/O-Conscious Data Preparation for Large-Scale Web Search Engines}},
author = {Lifantsev, Maxim and Chiueh, Tzi-cker},
journal = {PVLDB},
series = {{VLDB} '02},
doi = {10.1016/B978-155860869-6/50041-X},
url = {https://doi.org/10.1016/B978-155860869-6/50041-X},
year = {2002}
}
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