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antongiacomo committed Oct 23, 2024
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\section{System Model and Reference Scenario}\label{sec:requirements}
We present our system model (Section \ref{sec:systemmodel}) and our reference scenario (Section \ref{sec:service_definition}).
We present our system model (Section \ref{sec:systemmodel}) and our reference scenario (Section \ref{sec:service_definition}).

\subsection{System Model}\label{sec:systemmodel}
We consider a service-based environment where a service pipeline is designed to analyze data. Our system model is derived by a generic big-data framework and enriched with metadata specifying data protection requirements and functional specifications. It is composed of the following parties:
We consider a service-based environment where a service pipeline is designed to analyze data. Our system model is derived by a generic big-data framework and enriched with metadata specifying data protection requirements and functional specifications. It is composed of the following parties:
\begin{itemize}
\item \emph{Service}, a software distributed by a service provider that performs a specific task;
\item \emph{Service}, a software distributed by a service provider that performs a specific task;
\item \emph{Service Pipeline}, a sequence of connected services that collect, prepare, process, and analyze data in a structured and automated manner;
\item \emph{Data Governance Policy}, a structured set of privacy guidelines, rules, and procedures regulating data access, sharing, and protection;
\item \emph{Data Governance Policy}, a structured set of privacy guidelines, rules, and procedures regulating data access, sharing, and protection;
\item \emph{User}, executing an analytics pipeline on the data. We assume the user is authorized to perform this operation, either as the data owner or as a data processor with the owner's consent.
\item \emph{Dataset}, the data target of the analytics pipeline. We assume all data are ready for analysis, that is, they underwent a preparatory phase addressing issues such as missing values, outliers, and formatting discrepancies.
\item \emph{Dataset}, the data target of the analytics pipeline. We assume all data are ready for analysis, that is, they underwent a preparatory phase addressing issues such as missing values, outliers, and formatting discrepancies.
\end{itemize}

\vspace{0.5em}

A service pipeline is a graph formally defined as follows.
A service pipeline is a graph formally defined as follows.

\vspace{0.5em}

Expand All @@ -35,33 +35,32 @@ \subsection{System Model}\label{sec:systemmodel}

\subsection{Reference Scenario}\label{sec:service_definition}
Our reference scenario considers a service pipeline analyzing a dataset of individuals detained in the Department of Correction facilities in the state of Connecticut while awaiting trial\footnote{https://data.ct.gov/Public-Safety/Accused-Pre-Trial-Inmates-in-Correctional-Faciliti/b674-jy6w}.

\cref{tab:dataset} presents a sample of the adopted dataset. Each row represents an inmate; each column includes the following attributes: date of download, a unique identifier, last entry date, race, gender, age of the individual, the bound value, offense, entry facility, and detainer. To serve the objectives of our study, we extended this dataset by introducing randomly generated first and last names.

\begin{table*}[!t]
\caption{Dataset sample}
\label{tab:dataset}
\centering
\begin{adjustbox}{max totalsize={.99\linewidth}{\textheight},center}
\bgroup
\def\arraystretch{1.5}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|}
\hline
\textbf{DOWNLOAD DATE} & \textbf{ID} & \textbf{FNAME} & \textbf{LNAME} & \textbf{LAD} & \textbf{RACE} & \textbf{GENDER} & \textbf{AGE} & \textbf{BOND} & \textbf{OFFENSE} & \textbf{\dots} \\ \hline
05/15/2020 & ZZHCZBZZ & ROBERT & PIERCE & 08/16/2018 & BLACK & M & 27 & 150000 & CRIMINAL POSS \dots & \dots \\ \hline
05/15/2020 & ZZHZZRLR & KYLE & LESTER & 03/28/2019 & HISPANIC & M & 41 & 30100 & VIOLATION OF P\dots & \dots \\ \hline
05/15/2020 & ZZSRJBEE & JASON & HAMMOND & 04/03/2020 & HISPANIC & M & 21 & 150000 & CRIMINAL ATTEM\dots & \dots \\ \hline
05/15/2020 & ZZHBJLRZ & ERIC & TOWNSEND & 01/15/2020 & WHITE & M & 36 & 50500 & CRIM VIOL OF P\dots & \dots \\ \hline
05/15/2020 & ZZSRRCHH & MICHAEL & WHITE & 12/26/2018 & HISPANIC & M & 29 & 100000 & CRIMINAL ATTEM\dots & \dots \\ \hline
05/15/2020 & ZZEJCZWW & JOHN & HARPER & 01/03/2020 & WHITE & M & 54 & 100000 & CRIM VIOL OF P\dots & \dots \\ \hline
05/15/2020 & ZZHJBJBR & KENNETH & JUAREZ & 03/19/2020 & HISPANIC & M & 35 & 100000 & CRIM VIOL ST C\dots & \dots \\ \hline
05/15/2020 & ZZESESZW & MICHAEL & SANTOS & 12/03/2018 & WHITE & M & 55 & 50000 & ASSAULT 2ND, V\dots & \dots \\ \hline
05/15/2020 & ZZRCSHCZ & CHRISTOPHER & JONES & 05/13/2020 & BLACK & M & 43 & 10000 & INTERFERING WIT\dots & \dots \\ \hline
\end{tabular}
\egroup
\end{adjustbox}

\end{table*}
{\color{OurColor} In the adopted dataset each row represents an inmate}; each column includes the following attributes: date of download, a unique identifier, last entry date, race, gender, age of the individual, the bound value, offense, entry facility, and detainer. To serve the objectives of our study, we extended this dataset by introducing randomly generated first and last names.

% \begin{table*}[!t]
% \caption{Dataset sample}
% \label{tab:dataset}
% \centering
% \begin{adjustbox}{max totalsize={.99\linewidth}{\textheight},center}
% \bgroup
% \def\arraystretch{1.5}
% \begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|}
% \hline
% \textbf{DOWNLOAD DATE} & \textbf{ID} & \textbf{FNAME} & \textbf{LNAME} & \textbf{LAD} & \textbf{RACE} & \textbf{GENDER} & \textbf{AGE} & \textbf{BOND} & \textbf{OFFENSE} & \textbf{\dots} \\ \hline
% 05/15/2020 & ZZHCZBZZ & ROBERT & PIERCE & 08/16/2018 & BLACK & M & 27 & 150000 & CRIMINAL POSS \dots & \dots \\ \hline
% 05/15/2020 & ZZHZZRLR & KYLE & LESTER & 03/28/2019 & HISPANIC & M & 41 & 30100 & VIOLATION OF P\dots & \dots \\ \hline
% 05/15/2020 & ZZSRJBEE & JASON & HAMMOND & 04/03/2020 & HISPANIC & M & 21 & 150000 & CRIMINAL ATTEM\dots & \dots \\ \hline
% 05/15/2020 & ZZHBJLRZ & ERIC & TOWNSEND & 01/15/2020 & WHITE & M & 36 & 50500 & CRIM VIOL OF P\dots & \dots \\ \hline
% 05/15/2020 & ZZSRRCHH & MICHAEL & WHITE & 12/26/2018 & HISPANIC & M & 29 & 100000 & CRIMINAL ATTEM\dots & \dots \\ \hline
% 05/15/2020 & ZZEJCZWW & JOHN & HARPER & 01/03/2020 & WHITE & M & 54 & 100000 & CRIM VIOL OF P\dots & \dots \\ \hline
% 05/15/2020 & ZZHJBJBR & KENNETH & JUAREZ & 03/19/2020 & HISPANIC & M & 35 & 100000 & CRIM VIOL ST C\dots & \dots \\ \hline
% 05/15/2020 & ZZESESZW & MICHAEL & SANTOS & 12/03/2018 & WHITE & M & 55 & 50000 & ASSAULT 2ND, V\dots & \dots \\ \hline
% 05/15/2020 & ZZRCSHCZ & CHRISTOPHER & JONES & 05/13/2020 & BLACK & M & 43 & 10000 & INTERFERING WIT\dots & \dots \\ \hline
% \end{tabular}
% \egroup
% \end{adjustbox}

% \end{table*}

In this context, the user, a member of the Connecticut Department of Correction (DOC), is interested to compare the admission trends in Connecticut prisons with the ones in New York and New Hampshire. We assume that the three DOCs are partners and share data according to their privacy policies. Moreover, the policy specifies that the entire service execution must occur within the Connecticut Department of Correction. In case data transmission extends beyond Connecticut's borders, data protection measures must be implemented.

Expand Down Expand Up @@ -114,7 +113,7 @@ \subsection{Reference Scenario}\label{sec:service_definition}
\draw[->] (node3) -- (merge);
\draw[->] (merge) -- (node5);
\draw[->] (node5) -- (storage);

\draw[->] (storage) -- (visualization);

\end{tikzpicture}
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