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30_Thesis/sections/50_design_and_implementation.tex
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@@ -55,7 +55,7 @@ Data is stored and retrieved using data access objects. Therefore, the currently
\section{Database} \section{Database}
\label{sec:DesignImplementation:Database} \label{sec:DesignImplementation:Database}
The choice among database systems has to be made between \emph{non-relational} and \emph{relational} databases. Relational databases are good in regard to at the four ACID (atomicity, consistency, isolation, durability) principles \cite{chrysanthis1998recovery, cookACIDBASEDatabase2009}. Moreover, if the data structures are not changing it provides a solid basis that keeps the data reliable. A non-relational database on the other hand is ideal for rapid agile development. Moreover, it excels if data requirements are not entirely clear and if a large amount of unstructured data has to be stored. Moreover, non-relational databases allow the system to store the data in any kind of structure. This proves an advantage as it allows to use the same data structure to be stored that also has to be fed out through the api. Hence, parsing methods for the API can be reused and altered upon changing requirements. Moreover, the data used for the recommender is mostly not interconnected. Therefore a relational databases' main strength, the data structure, does not really come into play here. Thus, in this thesis a NoSQL database is used. The choice among database systems has to be made between \emph{non-relational} and \emph{relational} databases. Relational databases are good in regard to at the four ACID (atomicity, consistency, isolation, durability) principles \cite{chrysanthis1998recovery, cookACIDBASEDatabase2009}. Moreover, if the data structures are not changing it provides a solid basis that keeps the data reliable. A non-relational database on the other hand is ideal for rapid agile development. Besides, it excels if data requirements are not entirely clear and if a large amount of unstructured data has to be stored. Furthermore, non-relational databases allow the system to store the data in any kind of structure. This proves an advantage as it allows to use the same data structure to be stored that also has to be fed out through the api. Hence, parsing methods for the API can be reused and altered upon changing requirements. Moreover, the data used for the recommender is mostly not interconnected. Therefore a relational databases' main strength, the data structure, does not really come into play here. Thus, in this thesis a NoSQL database is used.
\section{Scoring Functions} \section{Scoring Functions}
\label{sec:DesignImplementation:ScroingFunctions} \label{sec:DesignImplementation:ScroingFunctions}
@@ -80,7 +80,7 @@ Visualisation is done using \emph{Matplotlib} \cite{MatplotlibDocumentation} in
\section{Requirements Assessment} \section{Requirements Assessment}
\label{sec:DesignImplementation:RequirementsAssesment} \label{sec:DesignImplementation:RequirementsAssesment}
The requirements posed in \autoref{sec:Concept:Requirements} are assessed in this section. The recommender uses a continuous value range between zero and one for preferences. Moreover, the recommendation component is designed to be used independently, therefore it is possible to use it without proprietary software. The requirements posed in \autoref{sec:Concept:Requirements} are assessed in this section. The recommender uses a continuous value range between zero and one for preferences. Additionally, the recommendation component is designed to be used independently, therefore it is possible to use it without proprietary software.
Additionally, the group scoring function takes into account preferences of each group member and the current configuration state. The recommendation system can be used by multiple users at the same time as all information needed for a recommendation is sent with the recommendation request. Accordingly, multiple group configurators can use the system at the same time. Furthermore, only valid solutions are recommended because the recommender system's configuration database stores finished configurations. Moreover, the system responds in a timely manner with all components running on the same machine, a modern laptop. Additionally, the group scoring function takes into account preferences of each group member and the current configuration state. The recommendation system can be used by multiple users at the same time as all information needed for a recommendation is sent with the recommendation request. Accordingly, multiple group configurators can use the system at the same time. Furthermore, only valid solutions are recommended because the recommender system's configuration database stores finished configurations. Moreover, the system responds in a timely manner with all components running on the same machine, a modern laptop.
Consequently, all mandatory requirements have been fulfilled. Consequently, all mandatory requirements have been fulfilled.