add sequence diagram showing communication for generating recommendations

30_Thesis/sections/50_design_and_implementation.tex

@@ -13,7 +13,7 @@ This thesis is looking at a recommendation system for a configurator. Developing
 He only makes changes to M.Customer which is renamed to M.Collab-Customer and introduces a new component M.Collab.
 
 \begin{description}
-    \item[M.Core] provides the base of the configurator. It checks the configuration against all rules in the database, provides possible alternatives if a change invalidates other parts of a configuration. The system relies on a CSP solver for validation and suggestion of alternatives.
+    \item[M.Core] provides the base of the configurator. It checks the configuration against all rules in the database, provides possible alternatives if a change invalidates other parts of a configuration. The system relies on a CSP solver for valida tion and suggestion of alternatives.
     \item[M.Model] is the editor to create products and rules. These rules can then be uploaded to M.Core.
     \item[M.Collab] is a node.js server application that communicates with M.Core via REST-API and with M.Collab-Customer via WebSocket. It sits in between M.Collab-Customer and M.Core and handles all processing regarding collaborative configuration.
     \item[M.Collab-Customer] a modified version of M.Customer that does all communication via WebSocket and does communicate with M.Collab instead of M.Core. M.Customer is the customer facing component. It allows a customer to configure a product or solution.
@@ -49,6 +49,18 @@ Another viable solution is adding the recommendation functionality into M.Core.
     \label{fig:DesignImplementation:UserInterface}
 \end{figure}
 
+When a recommendation is supposed to be generated M.Collab sends a REST request to M.Recommend (see \autoref{fig:DesignImplementation:SequenceDiagramRecommendation}). This request contains preferences and the current configuration. M.Recommend generates a recommendation based on the received data. The recommended finished configuration is now send via broadcast over WebSocket to all M.Customer clients. 
+
+
+\begin{figure}
+    \centering
+    \includegraphics[width=1\textwidth]{./figures/50_design_and_implementation/sequence_diagram_generating_recommendation.pdf}
+    \caption{A sequence diagram showing the recommendation process.}
+    \label{fig:DesignImplementation:SequenceDiagramRecommendation}
+\end{figure}
+
+
+
 \subsection{Scoring Functions}
 
 \section{Software Quality}

Additional_Notes/uml/sequence_diagram_generating_recommendation.plantuml

@@ -0,0 +1,36 @@
+@startuml
+skinparam monochrome true
+skinparam SequenceBoxBackgroundColor #ffffff
+skinparam ParticipantPadding 5
+skinparam shadowing false
+hide footbox
+
+title generating a recommendation 
+
+box "Client B"
+participant "M.Customer B"
+end box
+
+box "Client A"
+participant "M.Customer A"
+end box
+
+box "Server"
+participant M.Collab
+participant M.Recommend
+end box
+
+activate "M.Customer A"
+activate "M.Customer B"
+activate M.Collab
+
+M.Collab -> M.Recommend : get_recommendation(configuration, preferences)
+activate M.Recommend
+M.Collab <-- M.Recommend : return recommendedFeatures
+deactivate M.Recommend
+
+par
+    M.Collab --> "M.Customer A" : broadcast (recommendedFeatures)
+    M.Collab --> "M.Customer B" : broadcast (recommendedFeatures)
+end
+@enduml