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Finding Global Icebergs over Distributed Data Sets

Summary: Find global icebergs across many nodes despite items that are globally frequent but locally rare, avoiding prohibitive raw-data shipping. Introduce sampling and CountSketch-based distributed protocols with provable accuracy; CountSketch cuts communication by an order of magnitude while maintaining high accuracy. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1402
Venue
PODS
Year
2006
Pagerank
5.0605823e-05
Overall Rank
6,426 | 55.34%
DOI
-

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Showing 10 of 10 cited papers.

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

Rank Cited Paper Year Venue Pagerank
168 Approximate Frequency Counts over Data Streams 2002 VLDB 0.0003915627
598 Computing Iceberg Queries Efficiently 1998 VLDB 0.00019431661
743 Distributed Top-K Monitoring 2003 SIGMOD 0.00017318557
778 Spectral Bloom Filters 2003 SIGMOD 0.00016729191
846 Approximate Counts and Quantiles over Sliding Windows 2004 PODS 0.00015949293
874 What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically 2003 PODS 0.0001568356
1,006 Adaptive Filters for Continuous Queries over Distributed Data Streams 2003 SIGMOD 0.00014684829
1,136 Chain: Operator Scheduling for Memory Minimization in Data Stream Systems 2003 SIGMOD 0.00013745517
1,343 Scalable Distributed Stream Processing 2003 CIDR 0.00012478318
5,684 Distributed Set-Expression Cardinality Estimation 2004 VLDB 5.3731479e-05
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