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Anton Romanov 2024-07-29 16:18:40 +04:00
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@ -124,7 +124,7 @@ According to the diagram, the knowledge base consists of several levels of condi
According to figure \ref{fig:kb-structure}, the rule base will be represented as a hierarchical structure with two levels of rules. Previously, the authors obtained a structural model of the metadata $M$ of the integrated IS \cite{Kamaletdinova-2024}. According to figure \ref{fig:kb-structure}, the rule base will be represented as a hierarchical structure with two levels of rules. Previously, the authors obtained a structural model of the metadata $M$ of the integrated IS \cite{Kamaletdinova-2024}.
Thus, the first level will be represented as rules consisting of linguistic terms and will depend on changes in the meta-model. The second level of rules will be dynamically formed based on the results obtained at the first level. Thus, the first level will be represented as rules consisting of linguistic terms and will depend on changes in the meta-model. The second level of rules will be dynamically formed based on the results obtained at the first level.
Let $INP=\{INP_1,INP_2, ..., INP_z\}, z \in N$ be the set of linguistic terms representing the input data of the metadata model $M$, and $OUT= \allowbreak \{OUT_1, OUT_2, ...,\allowbreak OUT_w\}, w \in N$ be the set of linguistic terms representing the key processes of the metadata model $M$. Hence, the rule describing the first level will have a set-theoretic representation as follows: Let $INP=\{INP_1,INP_2, ..., INP_z\}, z \in N$ be the set of linguistic terms representing the input data of the metadata model $M$, and $OUT= \allowbreak \{OUT_1, ...,\allowbreak OUT_w\}, w \in N$ be the set of linguistic terms representing the key processes of the metadata model $M$. Hence, the rule describing the first level will have a set-theoretic representation as follows:
\begin{equation} \begin{equation}
P(INP) \rightarrow \{INP^{OUT_s}\}, OUT_s, P(INP) \rightarrow \{INP^{OUT_s}\}, OUT_s,
\end{equation} \end{equation}
@ -191,7 +191,7 @@ First, on the basis of the metamodel, the first level of the rule base is formed
The algorithm presented in Figure \ref{fig:algorithm} consists of the following steps: The algorithm presented in Figure \ref{fig:algorithm} consists of the following steps:
\begin{itemize} \begin{itemize}
\item Input data, represented as key-value data tuples from data storage of IS (e.g. $inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, ..., INP_z\},$ $z \in N$, figure \ref{fig:sources}. \item Input data, represented as key-value data tuples from data storage of IS (e.g. $inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, ..., INP_z\},$ $z \in N$, figure \ref{fig:sources}, for detail see \cite{Kamaletdinova-2024-2}.
\item Using the first level rule base (abstract level rule base) and the transformed input data ($INP$), a logical inference is performed, represented as $\{\{INP^{OUT_s}\}, OUT_s\}, s \in N$. \item Using the first level rule base (abstract level rule base) and the transformed input data ($INP$), a logical inference is performed, represented as $\{\{INP^{OUT_s}\}, OUT_s\}, s \in N$.