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Finding Critical Thresholds for Defining Bursts

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Data Warehousing and Knowledge Discovery (DaWaK 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6862))

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Abstract

A burst, i.e., an unusally high frequency of an event in a time-window, is interesting in monitoring systems as it often indicates abnormality. While the detection of bursts is well addressed, the question of what “critical” thresholds, on the number of events as well as on the window size, make a window “unusally bursty” remains a relevant one. The range of possible values for either threshold can be very large. We formulate finding the combination of critical thresholds as a 2D search problem and design efficient deterministic and randomized divide-and-conquer heuristics. For both, we show that under some weak assumptions, the computational overhead in the worst case is logarithmic in the sizes of the ranges. Our simulations show that on average, the randomized heuristic beats its deteministic counterpart in practice.

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Lahiri, B., Akrotirianakis, I., Moerchen, F. (2011). Finding Critical Thresholds for Defining Bursts. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, vol 6862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23544-3_37

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  • DOI: https://doi.org/10.1007/978-3-642-23544-3_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23543-6

  • Online ISBN: 978-3-642-23544-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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