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fix mistakes in design and implementation

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2020-04-30 10:54:47 +02:00
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30_Thesis/sections/50_design_and_implementation.tex
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@@ -3,7 +3,7 @@
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 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 preference slider that works in the interval $[0,1]$. The user receives feedback on what a value means based on the thumb position. The thumb rotates from thumbs down to neutral to thumbs up based on the selected value. Also functionality to show other participants in the session was added.
\autoref{fig:DesignImplementation:RecommenderForCollaborativeConfiguratorMerlin} shows 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 preference slider that works in the interval $[0,1]$. The user receives feedback on what a value means based on the thumb position. The thumb rotates from thumbs down to neutral to thumbs up based on the selected value. Also functionality to show other participants in the session was added.
\begin{figure}
\centering
@@ -18,7 +18,7 @@ This thesis requires a group configuration system with a recommender. In this th
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.
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 component 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 with multiple configurator instances. This is an advantage in the age of automatically scaling systems.
\begin{figure}
\centering
@@ -44,18 +44,18 @@ The REST API looks as follows.
\begin{description}
\item[/recommender/] is reachable via POST request and accepts a configuration state and preferences. Based on that a finished configuration is send back as response.
\item[/config/] accepts GET and POST requests. Sending a get requests results in a list of all stored configurations being send back. POST is used for adding a finished configuration to the configuration database.
\item[/config/] accepts GET and POST requests. Sending a GET request results in a list of all stored configurations being send back. POST is used for adding a finished configuration to the configuration database.
\item[/product\_structure/] is reachable via GET and PUT. GET returns the current product structure and PUT is there to replace it.
\end{description}
The API is implemented with a minimal amount of functions and the recommender currently only supports one product at the same time. However, the architecture is built in a way that can be easily extended into supporting multiple products.
The API is implemented with a minimal amount of functions and the recommender currently only supports one product at a time. However, the architecture is built in a way that it can be easily extended into supporting multiple products.
The general architecture adheres to the model view controller \todo[]{wir das Konzept im Litertaurteil eingeführt? Oder ist das ein allbekanntes Konzept in der Informatik? (mir wars neu)} inspired architecture.
Data is stored and retrieved using a data access objects therefore the currently used simple TinyDB database backend can be switched to a different one easily.
\section{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 great 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 allows the system to store the data in any kind of structure. This proves as an advantage as it allows to use the same data structure to be stored that also has to be fed out through the api. Therefore parsing methods for the API can be reused and changed 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 great 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 as an advantage as it allows to use the same data structure to be stored that also has to be fed out through the api. Therefore parsing methods for the API can be reused and changed 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}
\label{sec:DesignImplementation:ScroingFunctions}