Abstract
A novel method for analyzing time-series data and extracting time-correlations (time-dependent relationships) among multiple time-series data streams is described. The application of time-correlation detection in business impact analysis (BIA) is explained on an example. The method described in this paper is the first one that can efficiently detect and report time-dependent relationships among multiple time-series data streams. Detected time-correlation rules explain how the changes in the values of one set of time-series data streams influence the values in another set of time-series data streams. Those rules can be stored digitally and fed into various data analysis tools, such as simulation, forecasting, impact analysis, etc., for further analysis of the data. Performance experiments showed that the described method is 95% accurate, and has a linear running time with respect to the amount of input data.
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© 2006 Springer-Verlag Berlin Heidelberg
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Sayal, M. (2006). Business Impact Analysis Using Time Correlations. In: Lee, J., Shim, J., Lee, Sg., Bussler, C., Shim, S. (eds) Data Engineering Issues in E-Commerce and Services. DEECS 2006. Lecture Notes in Computer Science, vol 4055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780397_14
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DOI: https://doi.org/10.1007/11780397_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35440-6
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