Breaking It Down: An In-depth Study of Index Advisors
Summary: Systematically decomposes index advisors into three workflow blocks, taxonomy, and evaluates 17 methods across 11 datasets/systems. An open-source testbed, ablations, and explainable ML expose robustness, scenario fit, and key performance drivers. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wei Zhou (Xiamen University)
- 2. Chen Lin (Xiamen University)
- 3. Xuanhe Zhou (Tsinghua University)
- 4. Guoliang Li (Tsinghua University)
BibTeX Citation
@article{zhou_vldb24,
title = {{Breaking It Down: An In-depth Study of Index Advisors}},
author = {Zhou, Wei and Lin, Chen and Zhou, Xuanhe and Li, Guoliang},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2405--2418},
doi = {10.14778/3675034.3675035},
url = {https://doi.org/10.14778/3675034.3675035},
year = {2024}
}
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