Sublinear methods for detecting periodic trends in data streams

Funda Ergun, S. Muthukrishnan, S. Cenk Sahinalp

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations

Abstract

We present sublinear algorithms -algorithms that use significantly less resources than needed to store or process the entire input stream - for discovering representative trends in data streams in the form of periodicities. Our algorithms involve sampling Õ(√n) positions. and thus they scan not the entire data stream but merely a sublinear sample thereof. Alternately, our algorithms may be thought of as working on streaming inputs where each data item is seen once, but we store only a sublinear - Õ(√n) - size sample from which we can identify periodicities. In this work we present a variety of definitions of periodicities of a given stream, present sublinear sampling algorithms for discovering them, and prove that the algorithms meet our specifications and guarantees. No previously known results can provide such guarantees for finding any such periodic trends. We also investigate the relationships between these different definitions of periodicity.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMartin Farach-Colton
PublisherSpringer Verlag
Pages16-28
Number of pages13
ISBN (Print)3540212582, 9783540212584
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2976
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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