SourceSight: Enabling Effective Source Selection
Summary: SourceSight enables interactive exploration of vast, heterogeneous data sources for integration tasks. It introduces a novel multi-level source quality index and techniques to discover and evaluate sources, enabling efficient, granularity-aware source selection. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Theodoros Rekatsinas (Stanford University)
- 2. Amol Deshpande (University of Maryland)
- 3. Xin Luna Dong (Google)
- 4. Lise Getoor (University of California Santa Cruz)
- 5. Divesh Srivastava (AT&T)
BibTeX Citation
@inproceedings{rekatsinas_sigmod16,
title = {{SourceSight: Enabling Effective Source Selection}},
author = {Rekatsinas, Theodoros and Deshpande, Amol and Dong, Xin Luna and Getoor, Lise and Srivastava, Divesh},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2899403},
url = {https://dl.acm.org/doi/10.1145/2882903.2899403},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,125 | Computational Fact Checking: A Content Management Perspective | 2018 | VLDB | 5.6960323e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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