ExDRa: Exploratory Data Science on Federated Raw Data
Summary: ExDRa enables exploratory data science on federated raw data: ad-hoc integration, intermediates reuse, and lifecycle optimization for partially accessible data. It adds a federated SystemDS backend for linear algebra, PS, and data prep to enable enterprise federated ML and privacy-aware data management. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sebastian Baunsgaard (Graz University of Technology)
- 2. Matthias Boehm (Graz University of Technology)
- 3. Ankit Chaudhary (Technical University of Berlin)
- 4. Behrouz Derakhshan (German National Research Center for Information Technology)
- 5. Stefan Geißelsöder (Siemens)
- 6. Philipp M. Grulich (Technical University of Berlin)
- 7. Michael Hildebrand (Siemens)
- 8. Kevin Innerebner (Graz University of Technology)
- 9. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)
- 10. Claus Neubauer (Siemens)
- 11. Sarah Osterburg (Siemens)
- 12. Olga Ovcharenko (Graz University of Technology)
- 13. Sergey Redyuk (Technical University of Berlin)
- 14. Tobias Rieger (Graz University of Technology)
- 15. Alireza Rezaei Mahdiraji (German National Research Center for Information Technology)
- 16. Sebastian Benjamin Wrede (Graz University of Technology)
- 17. Steffen Zeuch (German National Research Center for Information Technology)
BibTeX Citation
@inproceedings{baunsgaard_sigmod21,
title = {{ExDRa: Exploratory Data Science on Federated Raw Data}},
author = {Baunsgaard, Sebastian and Boehm, Matthias and Chaudhary, Ankit and Derakhshan, Behrouz and Geißelsöder, Stefan and Grulich, Philipp M. and Hildebrand, Michael and Innerebner, Kevin and Markl, Volker and Neubauer, Claus and Osterburg, Sarah and Ovcharenko, Olga and Redyuk, Sergey and Rieger, Tobias and Mahdiraji, Alireza Rezaei and Wrede, Sebastian Benjamin and Zeuch, Steffen},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457549},
url = {https://dl.acm.org/doi/10.1145/3448016.3457549},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,538 | UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads | 2022 | VLDB | 5.8477764e-05 |
| 7,160 | DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines | 2022 | CIDR | 5.6855887e-05 |
| 8,456 | Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs | 2024 | VLDB | 5.4217837e-05 |
| 8,794 | AWARE: Workload-aware, Redundancy-exploiting Linear Algebra | 2023 | SIGMOD | 5.370464e-05 |
| 9,436 | GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example | 2023 | SIGMOD | 5.2692207e-05 |
| 9,473 | FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data | 2023 | SIGMOD | 5.2634238e-05 |
| 10,573 | Incremental Stream Query Deployment under Continuous Infrastructure Changes in the Cloud-Edge Continuum | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 37 of 37 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 3 | 13,557 | Big Data Science Needs Big Data Middleware | 2015 | CIDR |
| 4 | 8,045 | Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | 2024 | VLDB |
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| 9 | 9,977 | Towards Autonomous, Hands-Free Data Exploration | 2020 | CIDR |
| 10 | 1,756 | SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 2020 | CIDR |