| Improved range-summable random variable construction algorithms |
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Symposium on Discrete 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
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Authors
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A. R. Calderbank
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Princeton University, Princeton, New Jersey
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A. Gilbert
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University of Michigan, Ann Arbor, Michigan
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K. Levchenko
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University of California San Diego, La Jolla, California
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S. Muthukrishnan
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Rutgers University, Piscataway, New Jersey
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M. Strauss
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University of Michigan, Ann Arbor, Michigan
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Society for Industrial and Applied Mathematics
Philadelphia, PA, USA
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| Bibliometrics |
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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