Understanding local structure in ranked datasets
Summary: Defines “local structure” in ranked datasets—agreement of ranker subsets over item subsets—enabling localized consensus/conflict analysis yet hard to model and compute. Advocates combining declarative, incremental DB primitives with ML/data‑mining and outlines a roadmap for a scalable framework. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Julia Stoyanovich (Drexel University; Skolkovo Institute of Science and Technology)
- 2. Sihem Amer-Yahia (CNRS; Grenoble Institute of Technology)
- 3. Susan B. Davidson (University of Pennsylvania)
- 4. Marie Jacob (University of Pennsylvania)
- 5. Tova Milo (Tel Aviv University)
BibTeX Citation
@inproceedings{stoyanovich_cidr13,
address = {Amsterdam, Netherlands},
series = {{CIDR} '13},
title = {{Understanding local structure in ranked datasets}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Stoyanovich, Julia and Amer-Yahia, Sihem and Davidson, Susan B. and Jacob, Marie and Milo, Tova},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,717 | Rank aggregation with ties: Experiments and Analysis | 2015 | VLDB | 5.795458e-05 |
| 7,642 | A System for Management and Analysis of Preference Data | 2014 | VLDB | 5.5765908e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|
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