An improved data stream summary: the count-min sketch and its applications

From MaRDI portal
Publication:4675489

DOI10.1016/j.jalgor.2003.12.001zbMath1068.68048OpenAlexW2080234606WikidataQ61920316 ScholiaQ61920316MaRDI QIDQ4675489

No author found.

Publication date: 4 May 2005

Published in: Journal of Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jalgor.2003.12.001



Related Items

Sketched learning for image denoising, Compressive Learning for Patch-Based Image Denoising, Loda: lightweight on-line detector of anomalies, A Statistical Analysis of Probabilistic Counting Algorithms, Evaluating Bayesian Networks via Data Streams, The range 1 query (R1Q) problem, Summary Data Structures for Massive Data, Expander \(\ell_0\)-decoding, Improved algorithms for distributed entropy monitoring, Compressive statistical learning with random feature moments, Indexing for summary queries, RidgeSketch: A Fast Sketching Based Solver for Large Scale Ridge Regression, Phase transition in count approximation by count-min sketch with conservative updates, Joint tracking of multiple quantiles through conditional quantiles, Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis, Unnamed Item, Randomized counter-based algorithms for frequency estimation over data streams in \(O(\log \log N)\) space, Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory, Simplified Planar Coresets for Data Streams, Sublinear-space streaming algorithms for estimating graph parameters on sparse graphs, Arithmetic sketching, Forty years of frequent items, Top-\(k\) frequent items and item frequency tracking over sliding windows of any size, Estimating hybrid frequency moments of data streams, On deterministic sketching and streaming for sparse recovery and norm estimation, Unnamed Item, Voting almost maximizes social welfare despite limited communication, Streaming techniques and data aggregation in networks of tiny artefacts, Labeled graph sketches: keeping up with real-time graph streams, Sublinear Algorithms for MAXCUT and Correlation Clustering, Tracking the l_2 Norm with Constant Update Time, Buffered Count-Min Sketch on SSD: Theory and Experiments, Identifying correlated heavy-hitters in a two-dimensional data stream, Fast and accurate mining of correlated heavy hitters, Deterministic Heavy Hitters with Sublinear Query Time, Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows., On Low-Risk Heavy Hitters and Sparse Recovery Schemes, Range Majority in Constant Time and Linear Space, Accuracy vs. Lifetime: Linear sketches for aggregate queries in sensor networks, A parallel space saving algorithm for frequent items and the Hurwitz zeta distribution, Compressed sensing with sparse binary matrices: instance optimal error guarantees in near-optimal time, Deterministic \(k\)-set structure, Computer science and decision theory, The frequent items problem, under polynomial decay, in the streaming model, Unnamed Item, Unnamed Item, Finding frequent items over sliding windows with constant update time, Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery, Sketching information divergences, Compressed sensing and best 𝑘-term approximation, A framework for clustering massive graph streams, Space‐efficient tracking of persistent items in a massive data stream, Hierarchical sampling from sketches: Estimating functions over data streams, Distributed mining of time-faded heavy hitters, Periodicity and Cyclic Shifts via Linear Sketches, Streaming Algorithms with One-Sided Estimation, On the power of multiple anonymous messages: frequency estimation and selection in the shuffle model of differential privacy, A Note on Estimating Hybrid Frequency Moment of Data Streams, Multiscale Matrix Sampling and Sublinear-Time PageRank Computation, On Approximating Matrix Norms in Data Streams, An Implicit Representation and Iterative Solution of Randomly Sketched Linear Systems, Optimizing the confidence bound of count-min sketches to estimate the streaming big data query results more precisely, Frugal Streaming for Estimating Quantiles, Space-efficient estimation of statistics over sub-sampled streams