Cleaning up

.github/workflows/blank.yml

@@ -28,9 +28,6 @@ jobs:
 
       - name: Generate PDF document
         uses: hspaans/latexmk-action@v1
-        with:
-          format: pdf
-          filename: main.tex
 
       - name: Create Release
         id: create_release

Big Data Access Control - extension.code-workspace

@@ -203,6 +203,9 @@
 			".venv/lib/python3.11/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/pydevd_frame_evaluator.*.so": true,
 			".venv/lib/python3.11/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/pydevd_frame_evaluator.*.pyd": true,
 			".venv/lib/python3.11/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/pydevd_frame_evaluator.pyx": true
+		},
+		"files.associations": {
+			"*.tikz": "latex"
 		}
 	}
 }

main.tex

@@ -66,7 +66,10 @@
 \input{introduction}
 \input{motivations}
 \input{system_model}
-\input{service_composition}
+\input{pipeline_template.tex}
+\input{pipeline_template_example.tex}
+\input{pipeline_instance.tex}
+\input{pipeline_instance_example.tex}
 \input{metrics}
 \input{experiment}
 \input{related}

pipeline_instance_example.tex

@@ -51,9 +51,9 @@
 % \[ \forall S \in \mathrm{S}_{C}  \exists  \iChartFunction(S) = \mathrm{S}_{1} \]
 
 
-\begin{figure}
-  \centering
-  \includegraphics[width=\columnwidth]{serviceDetail.pdf}
-  \caption{Service Detail}
-  \label{fig:service_detail}reinstall remote-ssh
-\end{figure}
+% \begin{figure}
+%   \centering
+%   \includegraphics[width=\columnwidth]{serviceDetail.pdf}
+%   \caption{Service Detail}
+%   \label{fig:service_detail}reinstall remote-ssh
+% \end{figure}

pipeline_template.tex

@@ -76,33 +76,6 @@ \subsection{Pipeline Template Definition}\label{sec:templatedefinition}
 
 
 
-      \begin{figure}[ht!]
-        \centering
-        \begin{tikzpicture}[scale=0.9]
-          % Nodes
-          \node[draw ] (node1) at (0,1) {$\s{r}$};
-          \node[draw] (node2) at (0,2){$Data preparation $};
-          \node[draw] (node3) at (0,3) {$\timesOperator$};
-          \node[draw] (node4) at (-2,4) {$statistics$};
-          \node[draw] (node5) at (2,4) {$Machine Learning$};
-          \node[draw] (node6) at (0,5) {$\timesOperator$};
-          \node[draw] (node65) at (0,6) {$Storage$};
-
-          \draw[->] (node1) -- (node2);
-          \draw[->] (node2) -- (node3);
-          \draw[->] (node3) -- (node4);
-          \draw[->] (node3) -- (node5);
-          \draw[->] (node5) -- (node6);
-          \draw[->] (node4) -- (node6);
-          \draw[->] (node6) -- (node65);
-
-
-        \end{tikzpicture}
-        \caption{Pipeline Template}
-        \label{fig:service_composition_template}
-      \end{figure}
-
-
 
       % \begin{figure}[ht!]
       %   \centering

requirements.tex

@@ -1,5 +1,14 @@
 \section{Requirements and System Model}\label{sec:requirements}
-Big data is highly dependent on cloud-edge computing, which makes extensive use of multi-tenancy. Multi-tenancy permits sharing one instance of infrastructures, platforms or applications by multiple tenants to optimize costs. This leads to common scenarios where a service provider offers subscription-based analytics capabilities in the cloud, or a single data lake is accessed by multiple customers. Thus, it is a common situation to have a big data pipeline where data and services belong to various organizations, posing a serious risk of potential privacy and security violation. In the following of this section, we present our system model (Section \ref{sec:systemmodel}), the requirements driving our work (Section \ref{sec:accesscontrol_req}), and our reference scenario (Section \ref{sec:reference}).
+Big data is highly dependent on cloud-edge computing, which makes extensive use of multi-tenancy.
+Multi-tenancy permits sharing one instance of infrastructures,
+platforms or applications by multiple tenants to optimize costs.
+This leads to common scenarios where a service provider offers subscription-based analytics capabilities in the cloud,
+or a single data lake is accessed by multiple customers.
+Thus, it is a common situation to have a big data pipeline where data and services belong to various organizations,
+posing a serious risk of potential privacy and security violation.
+In the following of this section, we present our system model (Section \ref{sec:systemmodel}),
+the requirements driving our work (Section \ref{sec:accesscontrol_req}),
+and our reference scenario (Section \ref{sec:reference}).
 
 \subsection{System Model}\label{sec:systemmodel}
 Our system is a coalition of organizations that collaboratively execute a Big Data pipeline where \emph{i)} organizations join without necessarily integrating their cloud-based or on-premises ICT infrastructures, \emph{ii)} collaborative processes are carried out involving multi-party data collection and analytics, iii) the pipeline can be executed in a centralized or distributed deployment.

system_model.tex

@@ -62,12 +62,35 @@ \subsection{Service Pipeline and Reference Scenario}\label{sec:service_definitio
 
 We note that the template requires the execution of the entire service within a single country.
 If the data needs to be transmitted beyond the boundaries of Connecticut, data protection measures must be implemented.
-A visual representation of the flow is presented in Figure \ref{fig:service_composition_example}.
-
-\begin{figure}
-  \includegraphics[width=0.98\columnwidth]{service_composition_example}
-  \caption{Service composition example.}\label{fig:service_composition_example}
-
+A visual representation of the flow is presented in Figure \ref{fig:reference_scenario}.
+\begin{figure}[ht!]
+  \centering
+  \begin{tikzpicture}[scale=0.9]
+    % Nodes
+    \node[draw ] (node1) at (0,8) {$\s{r}$};
+    \node[draw] (node2) at (0,7){Data Preparation };
+    \node[draw] (node25) at (0,6){Data Enrichment};
+    \node[draw] (node3) at (0,5) {$\timesOperator$};
+    \node[draw] (node4) at (-2,4) {Data Analysis};
+    \node[draw] (node5) at (2,4) {Machine Learning};
+    \node[draw] (node6) at (0,3) {$\timesOperator$};
+    \node[draw] (node7) at (-2,2) {Data Storage};
+    \node[draw] (node8) at (2,2) {Data Visualization};
+    \node[draw] (node9) at (0,1) {$\timesOperator$};
+    \draw[->] (node1) -- (node2);
+    \draw[->] (node2) -- (node25);
+    \draw[->] (node25) -- (node3);
+    \draw[->] (node3) -- (node4);
+    \draw[->] (node3) -- (node5);
+    \draw[->] (node5) -- (node6);
+    \draw[->] (node4) -- (node6);
+    \draw[->] (node6) -- (node7);
+    \draw[->] (node6) -- (node8);
+    \draw[->] (node8) -- (node9);
+    \draw[->] (node7) -- (node9);
+  \end{tikzpicture}
+  \caption{Reference Scenario}
+  \label{fig:reference_scenario}
 \end{figure}
 
 The adopted dataset\footnote{https://data.ct.gov/Public-Safety/Accused-Pre-Trial-Inmates-in-Correctional-Faciliti/b674-jy6w} exhibits a straightforward row-and-column structure.