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Matrix Sketching Over Sliding Windows

Summary: Matrix sketching over sliding windows: time- and sequence-based continuous tracking of A^T A with l<<n. Shows linear-space needs for exact covariance in sliding windows vs O(d^2) streaming; proposes three frameworks—norm-sampling, Logarithmic Method, Dyadic Interval—with empirical validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5255
Venue
SIGMOD
Year
2016
Pagerank
4.9299348e-05
Overall Rank
6,774 | 52.88%
DOI
10.1145/2882903.2915228

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Showing 9 of 9 cited papers.

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

Rank Cited Paper Year Venue Pagerank
43 Models and Issues in Data Stream Systems 2002 PODS 0.00072723062
166 Approximate Frequency Counts over Data Streams 2002 VLDB 0.00039361552
402 Mergeable Summaries 2012 PODS 0.00024196343
848 Approximate Counts and Quantiles over Sliding Windows 2004 PODS 0.0001597308
1,346 Streaming Pattern Discovery in Multiple Time-Series 2005 VLDB 0.00012466288
2,427 Optimal Multi-scale Patterns in Time Series Streams 2006 SIGMOD 8.8370658e-05
2,878 Sampling Time-Based Sliding Windows in Bounded Space 2008 SIGMOD 7.9706235e-05
3,682 Database-friendly Random Projections 2001 PODS 6.8484436e-05
6,602 Continuous Matrix Approximation on Distributed Data 2014 VLDB 4.9971153e-05
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