Sequential change detection on data streams

S. Muthukrishnan, Eric Van Den Berg, Yihua Wu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Model-based declarative queries are becoming an attractive paradigm for interacting with many data stream applications. This has led to the development of techniques to accurately answer the queries using distributional models rather than raw values. The quintessential problem with this is that of detecting when there is a change in the input stream, which makes models stale and inaccurate. We adopt the sound statistical method of sequential hypothesis testing to study this problem on streams, without independence assumption. It yields algorithms that are fast, space-efficient, and oblivious to data's underlying distributions. Our experiments demonstrate the effectiveness of our methods to not only determine the existence of a change, but also the point where the change is initiated, relative to the ground truth we obtain. Our methods work seamlessly without window limitations inherent in prior work, thus have clearly shorter delays compared to alternative window-based solutions.

    Original languageEnglish (US)
    Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
    Number of pages6
    StatePublished - 2007
    Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
    Duration: Oct 28 2007Oct 31 2007

    Publication series

    NameProceedings - IEEE International Conference on Data Mining, ICDM
    ISSN (Print)1550-4786


    Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
    Country/TerritoryUnited States
    CityOmaha, NE

    ASJC Scopus subject areas

    • General Engineering


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