Information systems face significant challenges in today’s constantly evolving digital environments, including dynamic data, heterogeneous sources, and analytical complexities, which directly impact decision-making processes and organizational competitiveness. Data alignment, the process of aligning different sources using their schema and instances, has become a vital solution for ensuring data consistency and enabling effective data exploration. However, existing methods often rely on static approaches which lack adaptability to dynamic data environments and require full recomputation with every change. This study provides an extended evaluation of our previously proposed incremental alignment approach, IDAGEmb, which leverages dynamic graph embedding techniques to refine alignments progressively. Unlike traditional static methods, IDAGEmb adapts to changes in real time, efficiently handling schema modifications and evolving data instances. Our evaluation highlights significant improvements in managing heterogeneous data, optimizing resource usage, and maintaining alignment accuracy in dynamic environments. By integrating incremental graph embeddings, this approach offers a solution for dynamic data environments, providing organizations with consistent and actionable insights. This work builds upon our earlier results, offering a new perspective on data alignment for evolving datasets and emphasizing the effectiveness of dynamic embedding techniques.
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