Estimating statistical aggregates on probabilistic data streams

T. S. Jayram, Andrew McGregor, S. Muthukrishnan, Erik Vee

    Research output: Contribution to journalArticle

    Abstract

    The probabilistic stream model was introduced by Jayram et al. [2007]. It is a generalization of the data stream model that is suited to handling probabilistic data, where each item of the stream represents a probability distribution over a set of possible events. Therefore, a probabilistic stream determines a distribution over a potentially exponential number of classical deterministic streams, where each item is deterministically one of the domain values. We present algorithms for computing commonly used aggregates on a probabilistic stream. We present the first one pass streaming algorithms for estimating the expected mean of a probabilistic stream. Next, we consider the problem of estimating frequency moments for probabilistic data. We propose a general approach to obtain unbiased estimators working over probabilistic data by utilizing unbiased estimators designed for standard streams. Applying this approach, we extend a classical data stream algorithm to obtain a one-pass algorithm for estimating F2, the second frequency moment.We present the first known streaming algorithms for estimating F0, the number of distinct items on probabilistic streams. Our work also gives an efficient one-pass algorithm for estimating the median, and a two-pass algorithm for estimating the range.

    Original languageEnglish (US)
    Article number26
    JournalACM Transactions on Database Systems
    Volume33
    Issue number4
    DOIs
    StatePublished - Nov 1 2008

    Keywords

    • Frequency moments
    • Mean
    • Median
    • OLAP
    • Probabilistic streams

    ASJC Scopus subject areas

    • Information Systems

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