Towards Scalable Dataframe Systems
Summary: Scalable dataframe systems via MODIN; scaling pandas-like APIs with a simple dataframe data model and algebra. Signature features: flexible schemas, ordering, row/column equivalence, data/metadata fluidity; a trial-and-error interaction model spurs open data-management research. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Devin Petersohn (University of California Berkeley)
- 2. Stephen Macke (University of California Berkeley)
- 3. Doris Xin (University of California Berkeley)
- 4. William Ma (University of California Berkeley)
- 5. Doris Lee (University of California Berkeley)
- 6. Xiangxi Mo (University of California Berkeley)
- 7. Joseph E. Gonzalez (University of California Berkeley)
- 8. Joseph M. Hellerstein (University of California Berkeley)
- 9. Anthony D. Joseph (University of California Berkeley)
- 10. Aditya Parameswaran (University of California Berkeley)
BibTeX Citation
@article{petersohn_vldb20,
title = {{Towards Scalable Dataframe Systems}},
author = {Petersohn, Devin and Macke, Stephen and Xin, Doris and Ma, William and Lee, Doris and Mo, Xiangxi and Gonzalez, Joseph E. and Hellerstein, Joseph M. and Joseph, Anthony D. and Parameswaran, Aditya},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {11},
pages = {2033--2046},
doi = {10.14778/3407790.3407807},
url = {https://doi.org/10.14778/3407790.3407807},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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