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Summarizing and Mining Inverse Distributions on Data Streams via Dynamic Inverse Sampling

Summary: Formalizes management and mining of inverse distributions on data streams, showing forward and inverse views diverge under approximation. Proposes a dynamic inverse-sampling framework with provable guarantees for quantiles, equidepth histograms, heavy hitters, and rare-item counts, validated on network data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9307
Venue
VLDB
Year
2005
Pagerank
9.1073603e-05
Overall Rank
2,282 | 84.13%
DOI
-

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