Approximate Medians and other Quantiles in One Pass and with Limited Memory.

@inproceedings{DBLP:conf/sigmod/RajagopalanML98,
  author    = {Gurmeet Singh Manku and
               Sridhar Rajagopalan and
               Bruce G. Lindsay},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Approximate Medians and other Quantiles in One Pass and with
               Limited Memory},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {426-435},
  ee        = {http://doi.acm.org/10.1145/276304.276342, db/conf/sigmod/RajagopalanML98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We present new algorithms for computing approximate quantiles of large datasets in a single pass. The approximation guarantees are explicit, and apply without regard to the value distribution or the arrival distribution of the dataset. The main memory requirements are smaller than those reported earlier by an order of magnitude.

We also discuss methods that couple the approximation algorithms with random sampling to further reduce memory requirements. With sampling, the approximation guarantees are explicit but probabilistic, i.e. they apply with respect to a (user controlled) confidence parameter.

We present the algorithms, their theoretical analysis and simulation results.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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