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The Frequent Items Problem in Online Streaming under Various Performance Measures

  • Joan Boyar
  • Kim S. Larsen
  • Abyayananda Maiti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8070)

Abstract

In this paper, we strengthen the competitive analysis results obtained for a fundamental online streaming problem, the Frequent Items Problem. Additionally, we contribute with a more detailed analysis of this problem, using alternative performance measures, supplementing the insight gained from competitive analysis. The results also contribute to the general study of performance measures for online algorithms. It has long been known that competitive analysis suffers from drawbacks in certain situations, and many alternative measures have been proposed. However, more systematic comparative studies of performance measures have been initiated recently, and we continue this work, using competitive analysis, relative interval analysis, and relative worst order analysis on the Frequent Items Problem.

Keywords

Input Sequence Competitive Ratio Online Algorithm Frequent Item Competitive Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Joan Boyar
    • 1
  • Kim S. Larsen
    • 1
  • Abyayananda Maiti
    • 1
  1. 1.University of Southern DenmarkOdenseDenmark

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