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Sliding-Window Top-k Queries on Uncertain Streams

Summary: Unifies sliding-window top-k processing for uncertain streams, handling arrivals and expirations under tight space and time bounds. A single framework supports all top-k definitions, yielding compact synopses smaller than the window with efficient processing, validated on synthetic and real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9677
Venue
VLDB
Year
2008
Pagerank
6.2765138e-05
Overall Rank
4,328 | 69.93%
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
1,159 k-Nearest Neighbors in Uncertain Graphs 2010 VLDB 0.00013584223
1,612 A Unified Approach to Ranking in Probabilistic Databases 2009 VLDB 0.00011142757
3,190 Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers 2009 SIGMOD 7.413338e-05
6,219 Reverse k-Ranks Query 2014 VLDB 5.1458862e-05
6,600 Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory 2024 SIGMOD 4.9925547e-05
6,632 Global Immutable Region Computation 2014 SIGMOD 4.9803909e-05
7,931 Optimal Approximate Matrix Multiplication over Sliding Windows 2026 VLDB 4.6089395e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

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