Add ML/DM OLAP and ETL

src/machine-learning-and-data-mining/main.tex

@@ -3,15 +3,16 @@
 \title{Machine Learning and Data Mining}
 \date{2023 -- 2024}
 
-\DeclareAcronym{oltp}{short=OLTP, long=On-Line Transaction Processing}
+\DeclareAcronym{oltp}{short=OLTP, long=Online Transaction Processing}
 \DeclareAcronym{erp}{short=ERP, long=Enterprise Resource Planning}
 \DeclareAcronym{mis}{short=MIS, long=Management Information System}
 \DeclareAcronym{dss}{short=DSS, long=Decision Support System}
 \DeclareAcronym{eis}{short=EIS, long=Executive Information System}
-\DeclareAcronym{olap}{short=OLAP, long=On-Line Analysical Processing}
+\DeclareAcronym{olap}{short=OLAP, long=Online Analysical Processing}
 \DeclareAcronym{bi}{short=BI, long=Business Intelligence}
 \DeclareAcronym{dwh}{short=DWH, long=Data Warehouse}
 \DeclareAcronym{dm}{short=DM, long=Data Mart}
+\DeclareAcronym{etl}{short=ETL, long=Extraction{,} Transformation{,} Loading}
 
 
 \begin{document}

src/machine-learning-and-data-mining/sections/_bi.tex

@@ -20,3 +20,172 @@
     \item[\Ac{dm}] \marginnote{\Acl{dm}}
         Subset of the primary \ac{dwh} with information relevant to a specific business area.
 \end{description}
+
+
+
+\section{\Acl{olap} (\Ac{olap})}
+
+\begin{description}
+    \item[\ac{olap} analyses] \marginnote{\Acl{olap} (\Ac{olap})}
+        Interactively navigate the information in a data warehouse.
+        Allows to visualize different levels of aggregation.
+
+    \item[\ac{olap} session] 
+        Navigation path created by the operations of a user.
+\end{description}
+
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.35\textwidth]{img/_olap_cube.pdf}
+    \caption{\ac{olap} data cube}
+\end{figure}
+
+
+\subsection{Operators}
+
+\begin{description}
+    \item[Roll-up] \marginnote{Roll-up}
+        Increases the level of aggregation (i.e. \texttt{GROUP BY} in SQL). Some details are collapsed together.
+
+    \item[Drill-down] \marginnote{Drill-down}
+        Reduces the level of aggregation. Some details are reintroduced.
+    
+    \item[Slide-and-dice] \marginnote{Slide-and-dice}
+        The slice operator reduces the number of dimensions (i.e. drops columns).
+
+        The dice operator reduces the number of data being analyzed (i.e. \texttt{LIMIT} in SQL).
+
+    \item[Pivot] \marginnote{Pivot}
+        Changes the layout of the data to analyze it from a different viewpoint.
+
+    \item[Drill-across] \marginnote{Drill-across}
+        Links concepts from different data sources (i.e. \texttt{JOIN} in SQL).
+
+    \item[Drill-through] \marginnote{Drill-through}
+        Switches from multidimensional aggregated data to operational data (e.g. a spreadsheet).
+\end{description}
+
+\begin{figure}[ht]
+    \begin{subfigure}{.33\textwidth}
+        \centering
+        \includegraphics[width=.60\linewidth]{img/olap_rollup.png}
+        \caption{\ac{olap} roll-up}
+    \end{subfigure}%
+    \begin{subfigure}{.33\textwidth}
+        \centering
+        \includegraphics[width=.60\linewidth]{img/olap_drilldown.png}
+        \caption{\ac{olap} drill-down}
+    \end{subfigure}
+    \begin{subfigure}{.33\textwidth}
+        \centering
+        \includegraphics[width=.80\linewidth]{img/olap_slicedice.png}
+        \caption{\ac{olap} slide-and-dice}
+    \end{subfigure}
+    \\
+    \begin{subfigure}{.5\textwidth}
+        \centering
+        \includegraphics[width=.35\linewidth]{img/olap_pivot.png}
+        \caption{\ac{olap} pivot}
+    \end{subfigure}
+    \begin{subfigure}{.5\textwidth}
+        \centering
+        \includegraphics[width=.35\linewidth]{img/olap_drillacross.png}
+        \caption{\ac{olap} drill-across}
+    \end{subfigure}
+    \\
+    \begin{subfigure}{\textwidth}
+        \centering
+        \includegraphics[width=.60\linewidth]{img/olap_drillthrough.png}
+        \caption{\ac{olap} drill-through}
+    \end{subfigure}
+\end{figure}
+
+
+
+\section{\Acl{etl} (\Ac{etl})}
+\marginnote{\Acl{etl} (\Ac{etl})}
+The \Ac{etl} process extracts, integrates and cleans operational data that will be loaded into a data warehouse.
+
+
+\subsection{Extraction}
+
+Extracted operational data can be:
+\begin{descriptionlist}
+    \item[Structured] \marginnote{Strucured data}
+        with a predefined data model (e.g. relational DB, CSV)
+
+    \item[Untructured] \marginnote{Unstrucured data}
+        without a predefined data model (e.g. social media content)
+\end{descriptionlist}
+
+Extraction can be of two types:
+\begin{descriptionlist}
+    \item[Static] \marginnote{Static extraction}
+        The entirety of the operational data are extracted to populate the
+        data warehouse for the first time.
+    
+    \item[Incremental] \marginnote{Incremental extraction}
+        Only changes applied since the last extraction are considered.
+        Can be based on a timestamp or a trigger.
+\end{descriptionlist}
+
+
+\subsection{Cleaning}
+
+Operational data may contain:
+\begin{descriptionlist}
+    \item[Duplicate data] 
+    \item[Missing data] 
+    \item[Improper use of fields] (e.g. saving the phone number in the \texttt{notes} field)
+    \item[Wrong values] (e.g. 30th of February)
+    \item[Inconsistency] (e.g. use of different abbreviations)
+    \item[Typos]    
+\end{descriptionlist}
+
+Methods to increase the quality of the data are:
+\begin{descriptionlist}
+    \item[Dictionary-based techniques] \marginnote{Dictionary-based cleaning}
+        Lookup tables to substitute abbreviations, synonyms or typos.
+        Applicable if the domain is known and limited.
+        
+    \item[Approximate merging] \marginnote{Approximate merging}
+        Merging data that do not have a common key.
+        \begin{description}
+            \item[Approximate join]
+                Use non-key attributes to join two tables (e.g. using the name and surname instead of an identifier).
+
+            \item[Similarity approach]
+                Use similarity functions (e.g. edit distance) to merge multiple instances of the same information
+                (e.g. typo in customer surname).
+        \end{description}
+    
+    \item[Ad-hoc algorithms] \marginnote{Ad-hoc algorithms}
+\end{descriptionlist}
+
+
+\subsection{Transformation}
+Data are transformed to respect the format of the data warehouse:
+\begin{descriptionlist}
+    \item[Conversion] \marginnote{Conversion}
+        modifications of types and formats (e.g. date format)
+    
+    \item[Enrichment] \marginnote{Enrichment}
+        creating new information by using existing attributes (e.g. compute profit from receipts and expenses)
+
+    \item[Separation and concatenation] \marginnote{Separation and concatenation}
+        Denormalization of the data: introduces redundances (i.e. breaks normal form\footnote{\url{https://en.wikipedia.org/wiki/Database_normalization}}) 
+        to speed up operations.
+\end{descriptionlist}
+
+
+\subsection{Loading}
+Adding data into a data warehouse:
+\begin{descriptionlist}
+    \item[Refresh] \marginnote{Refresh loading}
+        The entire \ac{dwh} is rewritten.
+
+    \item[Update] \marginnote{Update loading}
+        Only the changes are added to the \ac{dwh}. Old data is not modified.
+\end{descriptionlist}
+
+