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30_Thesis/sections/50_design_and_implementation.tex
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\chapter{Design and Implementation}
\label{ch:DesignImplementation}
This thesis requires a group configuration system with a recommender. Developing a configurator and a recommendation system for this thesis is infeasible. Therefore, the decision was made to use an existing configurator. In this thesis \emph{CAS Configurator Merlin} \cite{IndustrySpecificProduct2020} is extended. As this configurator does not allow group configuration, a modified version is used that already has been extended by \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}. The base system and its extension are described in \autoref{sec:Foundations:BaseSystem}.
This thesis requires a group configuration system with a recommender. In this thesis \emph{CAS Configurator Merlin} \cite{IndustrySpecificProduct2020} is extended. As this configurator does not allow group configuration, a modified version is used that has been developed by \citeauthor{raabKollaborativeProduktkonfigurationEchtzeit2019} \cite{raabKollaborativeProduktkonfigurationEchtzeit2019}. The base system and its extension are described in \autoref{sec:Foundations:BaseSystem}.
\autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin} shows how the system looks like after the recommender is added. For this thesis M.Collab and M.Collab-Customer have undergone some slight modifications to allow the displaying of recommendations and to query the recommender. The user interface is shown in \autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}. It extends the user interface of M.Collab-Customer by adding a like and dislike button and functionality to show other participants in the session.
\autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin} shows how the system architecture after the recommender is added. For this thesis M.Collab and M.Collab-Customer have undergone some slight modifications to allow the displaying of recommendations and to query the recommender. The user interface is shown in \autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin}. It extends the user interface of M.Collab-Customer by adding a like and dislike button \todo[]{bisher waren deine Präferenzen dargestellt über Werte im Bereich [0;1], jetzt taucht nur like/dislike auf. Erkläärung, wie neutral dargestellt wird.
Das musst du entweder ändern oder die Inkonsistenz gut wegargumentieren.} and functionality to show other participants in the session.
\begin{figure}
\centering
@@ -16,8 +17,8 @@ This thesis requires a group configuration system with a recommender. Developing
\section{Microservice}
\label{sec:DesignImplementation:Microservice}
An existing base system can be extended in different directions. As M.Collab already functions as middleman between M.Collab-Customer and M.Core, one solution would be to add recommender functionality into M.Collab-Customer. However this approach would conflict with the single responsibility and separation of concern software design principles. It would change M.Collab from a system that works similar to a router to one that additionally handles recommendations. M.Collab manages communication between M.Collab-Customer instances and between M.Core and M.Collab-Customer. Therefore this solution was discarded.
Another viable solution is adding the recommendation functionality into M.Core. The main benefit of this approach is that it allows the recommender to use systems that are used for solving constraint satisfaction problems to enhance recommendations. The disadvantage of this approach is however, that now the recommender is tightly coupled with the configurator. Not only does this mean that reproducing results in this thesis is only possible with access to a non open-source product but it also means that there is no possibility to use the recommender for other configuration solutions. Moreover no plugin API exists and therefore the extension would require additional effort.
An existing base system can be extended in different directions. As M.Collab already functions as middleman between M.Collab-Customer and M.Core, one solution would be to add recommender functionality into M.Collab-Customer. However, this approach would conflict with the single responsibility and separation of concern software design principles. It would change M.Collab from a system that works similar to a router to one that additionally handles recommendations. M.Collab manages communication between M.Collab-Customer instances and between M.Core and M.Collab-Customer. Therefore this solution was discarded.
Another viable solution is adding the recommendation functionality into M.Core. The main benefit of this approach is that it allows the recommender to use systems that are used for solving constraint satisfaction problems to enhance recommendations. The disadvantage of this approach is however, that now the recommender is tightly coupled with the configurator. Not only does this mean that reproducing results in this thesis is only possible with access to a non open-source product but it also means that there is no possibility to use the recommender for other configuration solutions. Moreover no plugin API exists and therefore the extension would require a large effort.
Due to these reasons the decision was made to create a new microservice, called M.Recommend which communicates with M.Collab, thereby being loosely coupled with other system components. Additionally, this approach allows to add functionality that takes advantage of a constraint satisfaction problem solver by either communicating with one via an API or by integrating one directly into the recommender M.Recommend. Moreover, it also allows the use of different technologies instead of JavaScript and Node.js. Using a separate component also allows to scale the system differently as it is possible to use multiple recommender instances for one configurator instance or one recommender instance for multiple configurator instances. This is an advantage in the age of automatically scaling systems.
\begin{figure}