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Finding Hierarchical Heavy Hitters in Data Streams

Summary: Defines Hierarchical Heavy Hitters (HHH) in data streams: report nodes whose discounted descendant counts reach phi of the total, under a hierarchy. Presents deterministic and randomized, hierarchy-aware algorithms that outperform naive approaches, with experiments showing improved accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
8997
Venue
VLDB
Year
2003
Pagerank
5.7580375e-05
Overall Rank
5,016 | 65.11%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
472 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.00022346384
865 What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically 2003 PODS 0.00015808172
1,655 Gigascope: High Performance Network Monitoring with an SQL Interface 2002 SIGMOD 0.00010997332
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