IDAGEmb: An Incremental Data Alignment Based on Graph Embedding

In the evolving digital environments, information systems are faced with a myriad of challenges such as data heterogeneity, the dynamic nature of data and integration complexities. These challenges impact on decision-making and data integration processes. We define data alignment as the process of aligning columns from different tabular sources using their schema and instances. Data alignment is emerging as an essential solution, ensuring data consistency between different sources and enabling effective integration and decision-making. However, existing solutions fail to take into account the dynamic nature of data in an incremental way. This study presents an incremental methodology that uses dynamic graph embedding techniques to progressively refine data alignments. Although the use of graph embedding techniques for data alignment is well established, their integration into incremental processing approaches remains less explored. This research attempts to fill this gap by evaluating the potential of incremental graph embedding techniques for data alignment. The adoption of this incremental technique has significantly improved the management of heterogeneous data in dynamic environments, while optimizing resource usage. Likewise, this study brings a new perspective to the field of data alignment at it aims to highlight the usefulness of dynamic embedding techniques for the exploration of dynamic datasets.