From 19b9b0f5da2dfdf8ecda09d429c67f92ac6eaa11 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Sun, 10 Feb 2013 14:52:13 -0800
Subject: intro

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 intro.tex | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'intro.tex')

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index 68eb17f..d43d62b 100644
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-In the classic setting of experimental design \cite{pukelsheim2006optimal,atkinson2007optimum},
+The statistical analysis of user data is a widely spread practice among Internet companies, which routinely use machine learning techniques over vast records of user data to perform inference and classification tasks integral to their daily operations. Statistical analysis of personal data collected through surveys or experimentation is also the cornerstone of marketing research, as well as research in a variety of experimental sciences such as medicine and sociology. 
+
+This state of affairs has motivated several recent studies of \emph{data markets}, in which an analyst wishes to purchase data from a set of users \cite{...}. Eah user discloses her data to the analyst only if she receives an appropriate compensation. Assuming that the analyst has a limited budget, a natural question to ask is how she should allocate her budget across different users. 
+
+ In the classic setting of experimental design \cite{pukelsheim2006optimal,atkinson2007optimum},
  an {\em experimenter}  \E\ has access to a population of $n$ potential experiment subjects. 
 Each subject $i\in  \{1,\ldots,n\}$ is associated with a set of parameters (or features) $x_i\in \reals^d$, 
 known to the experimenter. 
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