\chapter{Business Intelligence} \begin{description} \item[\Acl{bi}] \marginnote{\Acl{bi}} Transform raw data into information. Deliver the right information to the right people at the right time through the right channel. \item[\Ac{dwh}] \marginnote{\Acl{dwh}} Optimized repository that stores information for decision making processes. \Acp{dwh} are a specific type of \ac{dss}. Features: \begin{itemize} \item Subject-oriented: focused on enterprise specific concepts. \item Integrates data from different sources and provides an unified view. \item Non-volatile storage with change tracking. \end{itemize} \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}