Data streams: Algorithms and applications

S. Muthukrishnan

    Research output: Contribution to journalArticle

    Abstract

    In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [175].

    Original languageEnglish (US)
    Pages (from-to)117-236
    Number of pages120
    JournalFoundations and Trends in Theoretical Computer Science
    Volume1
    Issue number2
    DOIs
    StatePublished - Aug 2005

    ASJC Scopus subject areas

    • Theoretical Computer Science

    Fingerprint Dive into the research topics of 'Data streams: Algorithms and applications'. Together they form a unique fingerprint.

  • Cite this