Top-k Sorting Under Partial Order Information
Summary: Top-k sorting under partial orders with crowdsourced comparisons; minimizes expert workload. A dedicated top-k algorithm for PO, valid under two notions of the comparator, improving learning-to-rank with synthetic and real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Eyal Dushkin (Tel Aviv University)
- 2. Tova Milo (Tel Aviv University)
BibTeX Citation
@inproceedings{dushkin_sigmod18,
title = {{Top-k Sorting Under Partial Order Information}},
author = {Dushkin, Eyal and Milo, Tova},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3199672},
url = {https://dl.acm.org/doi/10.1145/3183713.3199672},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,948 | Interactive Graph Search | 2019 | SIGMOD | 5.6210529e-05 |
| 9,223 | Interactive Graph Search for Multiple Targets on DAGs | 2025 | VLDB | 5.1913816e-05 |
| 10,047 | Hierarchical Entity Resolution using an Oracle | 2022 | SIGMOD | 5.0786422e-05 |
| 10,049 | How to Design Robust Algorithms using Noisy Comparison Oracle | 2021 | VLDB | 5.0786422e-05 |
| 10,920 | Noisy Interactive Graph Search: An Uncertainty-Based Approach with Online Modeling of Latent Expertise and Difficulty | 2026 | VLDB | 4.9605343e-05 |
| 11,549 | k-Clustering with Comparison and Distance Oracles | 2024 | PODS | 4.9605343e-05 |
| 11,636 | Robust Best Point Selection under Unreliable User Feedback | 2024 | VLDB | 4.9605343e-05 |
| 12,059 | TQEL: Framework for Query-Driven Linking of Top-K Entities in Social Media Blogs | 2021 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 647 | Evaluating Top-k Selection Queries | 1999 | VLDB | 0.00015165562 |
| 754 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014226032 |
| 1,367 | A Unified Approach to Ranking in Probabilistic Databases | 2009 | VLDB | 0.00010878876 |
| 1,897 | Probabilistic Ranking of Database Query Results | 2004 | VLDB | 9.3988624e-05 |
| 2,275 | Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing | 2014 | VLDB | 8.703337e-05 |
| 4,029 | CLAMShell: Speeding up Crowds for Low-latency Data Labeling | 2016 | VLDB | 6.8285702e-05 |
| 4,941 | Crowdsourced Top-k Queries by Confidence-Aware Pairwise Judgments | 2017 | SIGMOD | 6.3314694e-05 |
| 5,052 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB | 6.2857835e-05 |
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|---|---|---|---|---|
| 1 | 6,191 | Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings | 2018 | VLDB |
| 2 | 2,904 | Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data | 2007 | VLDB |
| 3 | 12,372 | Efficient Top-k Indexing via General Reductions | 2016 | PODS |
| 4 | 7,413 | Efficient and Generic Evaluation of Ranked Queries | 2011 | SIGMOD |
| 5 | 12,357 | A Confidence-Aware Top-k Query Processing Toolkit on Crowdsourcing | 2017 | VLDB |
| 6 | 12,648 | Optimal Top-k Generation of Attribute Combinations based on Ranked Lists | 2012 | SIGMOD |
| 7 | 7,348 | Anytime Measures for Top-k Algorithms | 2007 | VLDB |
| 8 | 8,473 | Efficient Top-K Processing Over Query-Dependent Functions | 2008 | VLDB |
| 9 | 12,261 | A Rating-Ranking Method for Crowdsourced Top-k Computation | 2018 | SIGMOD |
| 10 | 5,052 | Crowdsourced Top-k Algorithms: An Experimental Evaluation | 2016 | VLDB |