Approximate Frequency Counts over Data Streams

By Gurmeet Singh Manku, Rajeev Motwani. Very Large Data Bases Conference, 2012

1,589 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/273c8b1a84c82845853ca5cfc2f2df955db03f93

How researchers see this paper

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In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Moment: maintaining closed frequent itemsets over a stream sliding window (2004, background): “Manku et al [14] proposed an approximate algorithm that for a given time t, mines frequent itemsets over the entiredata streams up to.”
Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation (2015, neutral): “The proof for the ﬁrst part of the statement can be derived following a similar analysis as [21] using label weights instead of frequency.”
Approximate counts and quantiles over sliding windows (2004, methodology): “[9] M. Datar, A. Gionis, P. Indyk, and R. Motwani.”
Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002, background): “University of New York at Buffalo, jianpei@cse.buffalo. edü IBM T. J. Watson Research Center, psyu@us.ibm.com”
Mining data streams: a review (2005, background): “Manku and Motwani [43] have proposed and implemented an approximate frequency counts in data streams.”
Space efficient mining of multigraph streams (2005, methodology): “This is the multigraph analog of heavy hitters [8, 26, 10] for graph or numerical streams, but harder, using more than the O( 1 ε log n) space solutions for non-multigraph streams [8, 26, 10].”
Sketch Algorithms for Estimating Point Queries in NLP (2012, methodology): “This scheme is inspired by lossy counting (Manku and Motwani, 2002).”
When Is the Right Time to Refresh Knowledge Discovered from Data? (2013, methodology): “2009), are inapplicable to the knowledge-refreshing problem because it is impractical to run KDD continuously to refresh knowledge due to the high cost of running KDD (Manku and Motwani 2002).”
Spectral bloom filters (2003, methodology): “Other methods, proposed in [11, 19] require a certain preprocessing the data given a static threshold.”
An improved data stream summary: the count-min sketch and its applications (2004, background): “A randomized algorithm given in [26] has expected space cost O( 1 ε log 1 φδ ), slightly better than our worst case space usage.”
Density-Based Clustering over an Evolving Data Stream with Noise (2006, background): “Analyzing and mining such kinds of data have been becoming a hot topic [1, 2, 4, 6, 10, 14].”