(Artificial) Mind over Matter: Integrating Humans and Algorithms in Solving Matching Problems
Summary: Human–machine collaboration in schema matching for data integration; questions the assumption that humans always lead. Big data, ML, IoT, and cloud enable a co-adaptive workflow where humans validate and steer automated matching. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Roee Shraga (Technion)
BibTeX Citation
@inproceedings{shraga_sigmod18,
title = {{(Artificial) Mind over Matter: Integrating Humans and Algorithms in Solving Matching Problems}},
author = {Shraga, Roee},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3183716},
url = {https://dl.acm.org/doi/10.1145/3183713.3183716},
year = {2018}
}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 699 | Data Integration with Uncertainty | 2007 | VLDB | 0.0001487423 |
| 1,497 | Generic Schema Matching, Ten Years Later | 2011 | VLDB | 0.00010568859 |
| 2,842 | Incremental Schema Matching | 2006 | VLDB | 8.0624613e-05 |
| 5,694 | Top-K Generation of Integrated Schemas Based on Directed and Weighted Correspondences | 2009 | SIGMOD | 6.1183376e-05 |
| 8,271 | Multi-Source Uncertain Entity Resolution at Yad Vashem: Transforming Holocaust Victim Reports into People | 2016 | SIGMOD | 5.4574671e-05 |
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