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diff --git a/content/background.tex b/content/background.tex index 6be66ea..d353069 100644 --- a/content/background.tex +++ b/content/background.tex @@ -64,7 +64,12 @@ As such, while the concept existed, the hardware did not catch up yet. However, in the last decade, multicore computing paradigms and the advent of GPU computing has finally enabled computation needed to run digital twins. As a result, digital twins have become more relevant today than 10 years ago~\cite{DBLP:conf/cirp/TAO2018169}. -A crucial part any of any \gls{dt} should be \emph{predictive modelling}, which drives actionable insights~\cite{DBLP:usdoe/report/AP26894}. +A crucial part any of any \gls{dt} should be \emph{predictive modelling}, which drives actionable insights~\cite{DBLP:usdoe/report/AP26894} (see \Cref{fig:predictive_analytics}). +\begin{figure}[t] + \includegraphics[width=\linewidth]{images/predictive_analytics.pdf} + \caption{Datacenter Digital Twin Diagram. There are 5 core elements to any Digital Twin: \myCircled{A} The Digital $\rightarrow$ Physical Twin link, \myCircled{B} the Physical Twin (\emph{e.g.,} the datacenter), \myCircled{C} the Physical $\rightarrow$ Digital Twin link, \myCircled{D} the Digital Twin, \myCircled{E} the features necessary to any Digital Twin.} + \label{fig:predictive_analytics} +\end{figure} Predictive modelling uses statistics to predict outcomes. When deployed commercially, for example in datacenters, predictive modelling is often referred to as predictive analytics~\cite{Wikipedia:PredictiveModelling}. Almost any statistical model can be used for prediction purposes, but nowadays predictive analysis is synonymous with machine learning. |