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Validating the INTERPRETOR Software Architecture for the Interpretation of Large and Noisy Data Sets
Abstract
In this chapter, the authors validate INTERPRETOR software architecture as a dataflow model of computation for filtering, abstracting, and interpreting large and noisy datasets with two detailed empirical studies from the authors’ former research endeavours. Also discussed are five further recent and distinct systems that can be tailored or adapted to use the software architecture. The detailed case studies presented are from two disparate domains that include intensive care unit data and building sensor data. By performing pattern mining on five further systems in the way the authors have suggested herein, they argue that INTERPRETOR software architecture has been validated.
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