section{Execution Loop in Pseudo-Code, declarative}

doc/mrva-overview.tex

@@ -395,6 +395,61 @@ while $Q \neq \emptyset$:
             # Server sends results for $(\mathcal{Q}, \mathcal{R}_i)$ back to the client
             $S$.send_results_to_client($C$, ($\mathcal{Q}$, $R_i$, $\mathcal{R}_i^{\mathcal{Q}}$))
 \end{lstlisting}
+\end{listing}
+\FloatBarrier
+
+\section{Execution Loop in Pseudo-Code, declarative}
+\begin{listing}[h] % h = here, t = top, b = bottom, p = page of floats
+  \caption{Distributed Query Execution Algorithm}
+
+\begin{lstlisting}[language=Python]
+# Distributed Query Execution with Agent Polling and Accumulated Results
+
+# Define initial state
+$\mathcal{R}$: set          # Set of repositories
+$\mathcal{Q}$: set          # Set of queries
+A: set          # Set of agents
+Q: list         # Queue of $(\mathcal{Q}, \mathcal{R}_i)$ pairs
+$\mathcal{R}_{\text{results}}$: dict = {}  # Mapping of repositories to their accumulated query results
+
+# Initialize result sets for each repository
+$\mathcal{R}_{\text{results}}$ = {$\mathcal{R}_i$: set() for $\mathcal{R}_i$ in $\mathcal{R}$}
+
+# Define job queue as an immutable mapping
+Q = [($\mathcal{Q}$, $\mathcal{R}_i$) for $\mathcal{R}_i$ in $\mathcal{R}$]
+
+# Processing as a declarative iteration over the job queue
+def execute_queries(agents, job_queue, repository_results):
+    def available_agents():
+        return {$\alpha$ for $\alpha$ in agents if $\alpha$.is_available()}
+
+    def process_job($\mathcal{Q}$, $\mathcal{R}_i$, $\alpha$):
+        results = {$\mathcal{Q}_j$: $\alpha$.execute($\mathcal{Q}_j$, $\mathcal{R}_i$) for $\mathcal{Q}_j$ in $\mathcal{Q}$}
+        return $\mathcal{R}_i$, results
+
+    def accumulate_results($\mathcal{R}_{\text{results}}$, $\mathcal{R}_i$, query_results):
+        return {**$\mathcal{R}_{\text{results}}$, $\mathcal{R}_i$: $\mathcal{R}_{\text{results}}$[$\mathcal{R}_i$] | set().union(*query_results.values())}
+
+    while job_queue:
+        active_agents = available_agents()
+        for $\alpha$ in active_agents:
+            $\mathcal{Q}$, $\mathcal{R}_i$ = job_queue[0]  # Peek at the first job
+            _, query_results = process_job($\mathcal{Q}$, $\mathcal{R}_i$, $\alpha$)
+            repository_results = accumulate_results(repository_results, $\mathcal{R}_i$, query_results)
+
+            $\alpha$.send_results(S, ($\mathcal{Q}$, $\mathcal{R}_i$, repository_results[$\mathcal{R}_i$]))
+            S.send_results_to_client(C, ($\mathcal{Q}$, $\mathcal{R}_i$, repository_results[$\mathcal{R}_i$]))
+
+        job_queue = job_queue[1:]  # Move to the next job
+
+    return repository_results
+
+# Execute the distributed query process
+$\mathcal{R}_{\text{results}}$ = execute_queries(A, Q, $\mathcal{R}_{\text{results}}$)
+\end{lstlisting}
+
+
+
 \end{listing}
 \FloatBarrier