Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms
Summary: Modularis proposes sub-operators as composable blocks for modular analytics across heterogeneous platforms. Minimal code changes enable portable performance across RDMA, serverless, storage, and outperforms Presto and SingleStore in end-to-end tests. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dimitrios Koutsoukos (ETH Zurich)
- 2. Ingo Müller (ETH Zurich)
- 3. Renato Marroquín (Oracle)
- 4. Ana Klimovic (ETH Zurich)
- 5. Gustavo Alonso (ETH Zurich)
BibTeX Citation
@article{koutsoukos_vldb21,
title = {{Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms}},
author = {Koutsoukos, Dimitrios and Müller, Ingo and Marroquín, Renato and Klimovic, Ana and Alonso, Gustavo},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {13},
pages = {3308--3321},
doi = {10.14778/3484224.3484229},
url = {https://doi.org/10.14778/3484224.3484229},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,823 | Query Processing on Tensor Computation Runtimes | 2022 | VLDB | 8.0893814e-05 |
| 4,908 | Using Cloud Functions as Accelerator for Elastic Data Analytics | 2023 | SIGMOD | 6.4488784e-05 |
| 6,253 | Declarative Sub-Operators for Universal Data Processing | 2023 | VLDB | 5.940599e-05 |
| 7,959 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB | 5.5181056e-05 |
| 8,053 | Maximus: A Modular Accelerated Query Engine for Data Analytics on Heterogeneous Systems | 2025 | SIGMOD | 5.4990605e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 38 of 38 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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