andras/great-ai
1
0
Fork 0
Code Issues Pull requests 1 Projects Releases 4 Packages Wiki Activity Actions

Minor consistency improvements

Browse source
This commit is contained in:
Andras Schmelczer 2022-09-18 16:13:51 +02:00
parent 790db8bb40
commit a73881a28e
No known key found for this signature in database
GPG key ID: 0EA1BC97D0AB076E
7 changed files with 11 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/thesis/chapters/4_design.tex
View file

@ -98,7 +98,7 @@ Although API design has been the central subject so far, it is worth remembering
The general theme in the implementation is that each explicit best practice should have its distinct, loosely-coupled functions or classes. When collaboration opportunities arise, such as persisting the model versions (\nth{1} component) into prediction traces (\nth{2} component), there are three primary conduits for realising them. These are the \texttt{context} object responsible for the global configuration per process, the \texttt{FunctionMetadataStore} specifying the expected behaviour of each prediction function, and finally the \texttt{TracingContext} that is created anew for each prediction input (session).
After refining the framework with feedback gathered from case studies and users, we will end up with the core architecture presented in Figure \ref{fig:architecture}. The implementation is mixed-paradigm, combining the expressiveness of functional and the design patterns of object-oriented programming (OOP) in order to maintain an overall low complexity. Reflection is also utilised, especially for run-time type-checking and generating the API definitions and Dashboard components. Regardless, the architecture is still presented with a syntax similar to the class diagrams of UML2 \cite{Rumbaugh2004} because it provides the freedom to express even the non-OOP design aspects.
After refining the framework with feedback gathered from case studies and users, we will end up with the core architecture presented in Figure \ref{fig:architecture}. The implementation is mixed-paradigm, combining the expressiveness of functional and the design patterns of object-oriented programming (OOP) in order to maintain an overall low complexity. Reflection is also utilised, especially for run-time type-checking and generating the API definitions and dashboard components. Regardless, the architecture is still presented with a syntax similar to the class diagrams of UML2 \cite{Rumbaugh2004} because it provides the freedom to express even the non-OOP design aspects.
For the sake of brevity, Figure \ref{fig:architecture} does not show all fields, and some related entities have been combined, e.g. the \textit{GroundTruthAPI} box represents the \texttt{add\_ground\_truth}, \texttt{query\_ground\_truth}, and \texttt{delete\_ground\_truth} functions. The client project can also access most of the presented entities, but these optional dependency arrows are not shown in the diagram. The \texttt{utilities} submodule is also left unexpanded; almost all of its functions are orthogonal with the exception of \texttt{parallel\_map}. The latter follows a textbook producer-consumer model facilitated by queues and event signals \cite{wang2020producer}.