License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2019.27
URN: urn:nbn:de:0030-drops-112429
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11242/
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Braverman, Vladimir ; Lang, Harry ; Ullah, Enayat ; Zhou, Samson

Improved Algorithms for Time Decay Streams

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Abstract

In the time-decay model for data streams, elements of an underlying data set arrive sequentially with the recently arrived elements being more important. A common approach for handling large data sets is to maintain a coreset, a succinct summary of the processed data that allows approximate recovery of a predetermined query. We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions.
We also consider the exponential time decay model for k-median clustering, where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm. Our algorithm stores O(k log(h Delta)+h) points where h is the half-life of the decay function and Delta is the aspect ratio of the dataset. Our techniques extend to k-means clustering and M-estimators as well.

BibTeX - Entry

@InProceedings{braverman_et_al:LIPIcs:2019:11242,
  author =	{Vladimir Braverman and Harry Lang and Enayat Ullah and Samson Zhou},
  title =	{{Improved Algorithms for Time Decay Streams}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{27:1--27:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Dimitris Achlioptas and L{\'a}szl{\'o} A. V{\'e}gh},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/11242},
  URN =		{urn:nbn:de:0030-drops-112429},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.27},
  annote =	{Keywords: Streaming algorithms, approximation algorithms, facility location and clustering}
}

Keywords: Streaming algorithms, approximation algorithms, facility location and clustering
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)
Issue Date: 2019
Date of publication: 17.09.2019

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