Amalur: Next-generation Data Integration in Data Lakes
Summary: Amalur rethinks data integration for ML-centric data lakes by unifying classic DI with factorized linear-algebra operators, pushing LA computation toward data sources. Enables ML-friendly extraction/prep with reduced ETL/export overhead and efficient factorized execution across joins. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Rihan Hai (Delft University of Technology)
- 2. Christos Koutras (Delft University of Technology)
- 3. Andra Ionescu (Delft University of Technology)
- 4. Asterios Katsifodimos (Delft University of Technology)
BibTeX Citation
@inproceedings{hai_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{Amalur: Next-generation Data Integration in Data Lakes}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Hai, Rihan and Koutras, Christos and Ionescu, Andra and Katsifodimos, Asterios},
year = {2022}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,235 | Towards Linear Algebra over Normalized Data | 2017 | VLDB | 0.00011548457 |
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