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Paf Paris edited untitled.tex
about 8 years ago
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\title{Extracts from various articles}
Appearing in a rather random order. Will tidy up later...
The problem was initially refer to as Extract - Transform - Load. The basic methodology was to:
\begin{itemize}
\item{construct a local schema}
\item{write a connector to do the extraction}
\item{write transformations to cleanup the data}
\item{load the data in the data warehouse}
\end{itemize}
Zachary G. Ives in \cite{cidr2015-Ives} says that a view \textit{at scale} yields many benefits, and this is evident in \cite{ieee-3-googlers}. "Follow the data. Choose a representation that can use unsupervised learning on unlabeled data, which is so much more plentiful than labeled data. Represent all the data with a nonparametric model rather than trying to summarize it with a parametric model, because with very large data sources, the data holds a lot of detail."
Georgia Kapitsaki in \cite{kapitsaki-2015} proposes a context extraction technique from existing datasets.