FTI: A Design Pattern for the Interpretation of Voluminous High Frequency and Noisy Data Sets

In many domains there is a need to interpret noisy and voluminous data. In this paper we propose and describe a new behavioural design pattern called FTI (Filter – Trender – Interpretation) for interpreting voluminous high frequency and noisy data sets. FTI consists of 3 consecutive processes: Filter which takes the original data and removes outliers and noise; Trender which derives trends from the filtered data; and Interpretation which uses knowledge bases to perform qualitative reasoning on the trends to provide an analysis of the original data. In this seminal paper we also show how FTI has successfully been applied to two different case studies.