As far as we know, this work is the first published algorithm that leverages the MapReduce framework for the validation of temporal logic properties on large event traces.
This paper presents a methodological framework for the validation of predictive simulations that synthesizes concepts from the fields of Conventional Model Verification and Validation, Testing of Scientific Theories, Design of Experiments, Error/Sensitivity, and Statistical Exploratory Data Analysis.
For the validation of the proposed framework, two ramp-up processes of an assembly station were emulated and their comparison was used to evaluate this work.
To summarize the validation of the framework for the presented datasets, the approach was four-fold: Confirm that the framework is able to identify anomalies with respect to those identified by Powersmiths, the dataset owner, themselves.
The validation of the framework is also described.
This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset.
A structured framework for verification and validation of the model dedicated to mode handling is proposed.
The motivation for this prototype is to provide a framework for demonstration and validation of the VDTN architecture, allowing the development, performance evaluation, and validation of new services and protocols, as well as VDTN applications.
