Periodicity testing with sublinear samples and space

Funda Ergun, S. Muthukrishnan, Cenk Sahinalp

    Research output: Contribution to journalArticlepeer-review


    In this work, we are interested in periodic trends in long data streams in the presence of computational constraints. To this end; we present algorithms for discovering periodic trends in the combinatorial property testing model in a data stream S of length n using o(n) samples and space. In accordance with the property testing model, we first explore the notion of being close to periodic by defining three different notions of self-distance through relaxing different notions of exact periodicity. An input S is then called approximately periodic if it exhibits a small self-distance (with respect to any one self-distance defined). We show that even though the different definitions of exact periodicity are equivalent, the resulting definitions of self-distance and approximate periodicity are not; we also show that these self-distances are constant approximations of each other. Afterwards, we present algorithms which distinguish between the two cases where S is exactly periodic and S is far from periodic with only a constant probability of error. Our algorithms sample only O(nlog2 n) (or O(nlog4 n), depending on the self-distance) positions and use as much space. They can also find, using o(n) samples and space, the largest/smallest period, and/or all of the approximate periods of S. These algorithms can also be viewed as working on streaming inputs where each data item is seen once and in order, storing only a sublinear (O(nlog 2 n) or O(nlog4 n)) size sample from which periodicities are identified.

    Original languageEnglish (US)
    Article number43
    JournalACM Transactions on Algorithms
    Issue number2
    StatePublished - Mar 1 2010


    • Combinatorial property testing
    • Periodicity

    ASJC Scopus subject areas

    • Mathematics (miscellaneous)


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