One-pass wavelet decompositions of data streams

Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, Martin J. Strauss

Research output: Contribution to journalArticlepeer-review

94 Scopus citations

Abstract

We present techniques for computing small space representations of massive data streams. These are inspired by traditional wavelet-based approximations that consist of specific linear projections of the underlying data. We present general "sketch"-based methods for capturing various linear projections and use them to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through the data and provide accurate representation as our experiments with real data streams show.

Original languageEnglish (US)
Pages (from-to)541-554
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume15
Issue number3
DOIs
StatePublished - May 2003

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Approximate queries
  • Data streams
  • Randomized algorithms
  • Wavelets

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