BlockJoin: Efficient Matrix Partitioning Through Joins
Summary: BlockJoin fuses relational and linear-algebra operators to emit block-partitioned results, cutting shuffles. Adapts columnar techniques (index-joins, late materialization) to dataflow engines, delivering 6x speedups and skew resilience vs Spark. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Andreas Kunft (Technical University of Berlin)
- 2. Asterios Katsifodimos (Delft University of Technology)
- 3. Sebastian Schelter (German National Research Center for Information Technology; Technical University of Berlin)
- 4. Tilmann Rabl (German National Research Center for Information Technology; Technical University of Berlin)
- 5. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
@article{kunft_vldb17,
title = {{BlockJoin: Efficient Matrix Partitioning Through Joins}},
author = {Kunft, Andreas and Katsifodimos, Asterios and Schelter, Sebastian and Rabl, Tilmann and Markl, Volker},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {13},
doi = {10.14778/3151106.3151110},
url = {https://doi.org/10.14778/3151106.3151110},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,756 | SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 2020 | CIDR | 9.8172465e-05 |
| 2,239 | An Intermediate Representation for Optimizing Machine Learning Pipelines | 2019 | VLDB | 8.8875753e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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