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Improved range-summable random variable construction algorithms
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Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms table of contents
Vancouver, British Columbia
SESSION: Session 9A table of contents
Pages: 840 - 849  
Year of Publication: 2005
ISBN:0-89871-585-7
Authors
A. R. Calderbank  Princeton University, Princeton, New Jersey
A. Gilbert  University of Michigan, Ann Arbor, Michigan
K. Levchenko  University of California San Diego, La Jolla, California
S. Muthukrishnan  Rutgers University, Piscataway, New Jersey
M. Strauss  University of Michigan, Ann Arbor, Michigan
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
: SIAM Activity Group on Discrete Mathematics
Publisher
Society for Industrial and Applied Mathematics  Philadelphia, PA, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 26,   Citation Count: 3
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ABSTRACT

Range-summable universal hash functions, also known as range-summable random variables, are binary-valued hash functions which can efficiently hash single values as well as ranges of values from the domain. They have found several applications in the area of data stream processing where they are used to construct sketches---small-space summaries of the input sequence.We present two new constructions of range-summable universal hash functions on n-bit strings, one based on Reed-Muller codes which gives k-universal hashing using O(nlog k) space and time for point operations and O(n2 log k) for range operations, and another based on a new subcode of the second-order Reed-Muller code, which gives 5-universal hashing using O(n) space, O(n log3 n) time for point operations, and O(n3) time for range operations.We also present a new sketch data structure using the new hash functions which improves several previous results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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S. Muthukrishnan. Data stream algorithms. http://www.cs.rutgers.edu/~muthu/stream-1-1.ps
 
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S. Muthukrishnan, M. Strauss. Maintenance of Multidimensional Histograms. FSTTCS 2003: 352--362.
 
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Collaborative Colleagues:
A. R. Calderbank: colleagues
A. Gilbert: colleagues
K. Levchenko: colleagues
S. Muthukrishnan: colleagues
M. Strauss: colleagues