Publication
Big Data 2017
Conference paper

Event clustering & event series characterization on expected frequency

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Abstract

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

Date

01 Jul 2017

Publication

Big Data 2017

Authors

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