J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan, "An Approximate L -Difference Algorithm for Massive Data Streams," Proc. IEEE Symp. Foundations of Computer Science (FOCS), pp. 501511, 1999. Home/Search Document Details and Download
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This paper is cited in the following contexts:
Processing Complex Aggregate Queries over Data Streams - Dobra, Garofalakis.. (2002) (30 citations) (Correct)
....basic idea of compact, pseudorandom sketching have also been proposed recently for other datastream applications. Gilbert et al. 15] propose the use of sketches for approximately computing one dimensional Haar wavelet coefficients and range aggregates over streaming numeric values. Strauss et al. [11] discuss sketch based techniques for the on line estimation of L differences between two numeric data streams. None of these earlier studies, however, has considered the hard technical problems involved in using sketching to effectively approximate the results of complex, multi join aggregate ....
J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan. "An Approximate L -Difference Algorithm for Massive Data Streams". In Proc. of the 40th Annual IEEE Symp. on Foundations of Computer Science, October 1999.
One-Pass Wavelet Decompositions of Data Streams - Gilbert, Kotidis.. (2003) (1 citation) Self-citation (Strauss) (Correct)
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J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan, "An Approximate L -Difference Algorithm for Massive Data Streams," Proc. IEEE Symp. Foundations of Computer Science (FOCS), pp. 501511, 1999.
Fast, Small-Space Algorithms for Approximate.. - Gilbert, Guha.. (2001) Self-citation (Strauss) (Correct)
.... at most B log N buckets and error at most that of the best B bucket histogram, in space O(B log(N) The most general data stream model (which we use in this paper) is known as the cash register model [6] Few algorithms are known for computing on the cash register model: estimating stream norms [2, 3, 11] and clustering [13] Computing histograms or other representations is significantly more involved than merely estimating the norm because the identification of the relevant coefficients is very crucially needed in our algorithm. Besides [6] 7] and [8] that we discuss next, no other nontrivial ....
Joan Feigenbaum, Sampath Kannan, Martin Strauss, Mahesh Viswanathan. An Approximate L1Difference Algorithm for Massive Data Streams. FOCS 1999, 501--511.
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