fixed quotes

main.tex

@@ -85,6 +85,7 @@
 \input{metrics}
 \input{experiment}
 \input{related}
+\input{declarations}
 
 \section{Conclusions}\label{sec:conclusions}
 In the realm of distributed data service pipelines, managing pipelines while ensuring both data quality and data protection presents numerous challenges. This paper proposed a framework specifically designed to address this dual concern. Our data governance model employs policies and continuous monitoring to address data security and privacy challenges, while preserving data quality, in service pipeline generation. The key point of the framework is in its ability to annotate each element of the pipeline with specific data protection requirements and functional specifications, then driving service pipeline construction. This method enhances compliance with regulatory standards and improves data quality by preserving maximum information across pipeline execution. Experimental results confirmed the effectiveness of our sliding window heuristic in addressing the computationally complex NP-hard service selection problem at the basis of service pipeline construction. Making use of a realistic dataset, our experiments evaluated the framework's ability to sustain high data quality while ensuring robust data protection, which is essential for pipelines where both data utility and privacy must coexist. To fully understand the impact of dataset selection on the retrieved quality and to ensure heuristic robustness across various scenarios, further investigation is planned for our future work. Future work will then %validate the findings of this paper and