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Comparing and Aggregating Rankings with Ties

Summary: Proposes principled metrics for comparing partial rankings with ties, gives efficient algorithms to compute them, and proves constant-factor relationships among the metrics. Formulates aggregation objectives and presents a database-access-aware algorithm that reads near-minimal entries to compute top-k of a near-optimal aggregate. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1308
Venue
PODS
Year
2004
Pagerank
8.9247846e-05
Overall Rank
2,385 | 83.41%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
1,258 Ordering the Attributes of Query Results 2006 SIGMOD 0.00013013676
2,813 Mining Search Engine Query Logs via Suggestion Sampling 2008 VLDB 8.0773142e-05
3,185 Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers 2009 SIGMOD 7.4192604e-05
4,637 Context-Sensitive Ranking 2006 SIGMOD 6.0303293e-05
4,803 A System for Management and Analysis of Preference Data 2014 VLDB 5.9107061e-05
5,700 Rank aggregation with ties: Experiments and Analysis 2015 VLDB 5.365274e-05
11,655 Top-k Queries over Digital Traces 2019 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
7 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015496097
709 Efficient Similarity Search and Classification via Rank Aggregation 2003 SIGMOD 0.00017768547
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