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