Estimating dominance norms of multiple data streams

Graham Cormode, S. Muthukrishnan

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    There is much focus in the algorithms and database communities on designing tools to manage and mine data streams. Typically, data streams consist of multiple signals. Formally, a stream of multiple signals is (i, ai,j) where i's correspond to the domain, j's index the different signals and a i,j ≥ 0 give the value of the jth signal at point i. We study the problem of finding norms that are cumulative of the multiple signals in the data stream. For example, consider the max-dominance norm, defined as ∑i maxj{ai,j}. It may be thought as estimating the norm of the "upper envelope" of the multiple signals, or alternatively, as estimating the norm of the "marginal" distribution of tabular data streams. It is used in applications to estimate the "worst case influence" of multiple processes, for example in IP traffic analysis, electrical grid monitoring and financial domain. In addition, it is a natural measure, generalizing the union of data streams or counting distinct elements in data streams. We present the first known data stream algorithms for estimating max-dominance of multiple signals. In particular, we use workspace and time-per-item that are both sublinear (in fact, poly-logarithmic) in the input size. In contrast other notions of dominance on streams a, b - min-dominance (∑i minj{a i,j}), countdominance (|{i|ai > bi}|) or relative-dominance (∑i ai/ max{1, bi}) - are all impossible to estimate accurately with sublinear space.

    Original languageEnglish (US)
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsGiuseppe di Battista, Uri Zwick
    PublisherSpringer Verlag
    Pages148-160
    Number of pages13
    ISBN (Print)3540200649, 9783540200642
    DOIs
    StatePublished - 2003

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2832
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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