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Fast and Scalable Data Transfer Across Data Systems
Summary: XDBC decomposes data transfer into modular components with pluggable implementations for heterogeneous systems. Automatic optimization of transfer configurations per workload yields up to 5x speedups vs generic tools, rivaling specialized approaches.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
7285
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,761 | 26.17%
DOI
10.1145/3725294
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
1.
Haralampos Gavriilidis
(Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
2.
Kaustubh Beedkar
(Indian Institute of Technology Delhi)
3.
Matthias Boehm
(Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
4.
Volker Markl
(Berlin Institute for the Foundations of Learning and Data; German National Research Center for Information Technology; Technical University of Berlin)
BibTeX Citation
Copy BibTeX
@inproceedings{gavriilidis_sigmod25,
title = {{Fast and Scalable Data Transfer Across Data Systems}},
author = {Gavriilidis, Haralampos and Beedkar, Kaustubh and Boehm, Matthias and Markl, Volker},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725294},
url = {https://dl.acm.org/doi/10.1145/3725294},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 38 of 38 cited papers.
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