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Data Quality Assessment
Data quality assessment is a burgeoning field in the area of data mining for applications in system identification. As the amount of data stored in industrial data historians increases exponentially, its usefulness is harder to manually determine. Manually parsing large data sets consisting of fifty or more variables sampled every minute for one or two years is practically impossible. Furthermore, it may be useful to automatically extract valuable data regions from a given data set for online process modelling, for example, in just-in-time modelling.
References
- Yuri A.W. Shardt (2012). Data Quality Assessment for Closed-Loop System Identification and Forecasting with Application to Soft Sensors, Doctoral Thesis, University of Alberta. Online access.