SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis
Summary: Introduce “splitting”: lossless join decomposition adding join keys to reduce dataframe redundancy while preserving a unified tabular view, no FD discovery required. SplitDF (Ibis/DuckDB) with automated SplitGen (Velox) achieves 19–61% memory savings with minimal API change. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Aarati Kakaraparthy (University of Wisconsin)
- 2. Jignesh M. Patel (Carnegie Mellon University)
BibTeX Citation
@article{kakaraparthy_vldb24,
title = {{SplitDF: Splitting Dataframes for Memory-Efficient Data Analysis}},
author = {Kakaraparthy, Aarati and Patel, Jignesh M.},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {9},
pages = {2175--2184},
doi = {10.14778/3665844.3665849},
url = {https://doi.org/10.14778/3665844.3665849},
year = {2024}
}
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