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Improved Algorithms for Time Decay Streams

Authors Vladimir Braverman, Harry Lang, Enayat Ullah, Samson Zhou



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Author Details

Vladimir Braverman
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Harry Lang
  • MIT CSAIL, Cambridge, MA, USA
Enayat Ullah
  • Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Samson Zhou
  • School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

Acknowledgements

This material is based upon work supported in part by the National Science Foundation under Grant No. 1447639, by the Google Faculty Award and by DARPA grant N660001-1-2-4014. Its contents are solely the responsibility of the authors and do not represent the official view of DARPA or the Department of Defense.

Cite AsGet BibTex

Vladimir Braverman, Harry Lang, Enayat Ullah, and Samson Zhou. Improved Algorithms for Time Decay Streams. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 27:1-27:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2019.27

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Facility location and clustering
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Streaming algorithms
  • approximation algorithms
  • facility location and clustering

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