  Using data warehouses to analyse multidimensional data is a significant task
in company decision-making.The data warehouse merging process is composed of
two steps: matching multidimensional components and then merging them. Current
approaches do not take all the particularities of multidimensional data
warehouses into account, e.g., only merging schemata, but not instances; or not
exploiting hierarchies nor fact tables. Thus, in this paper, we propose an
automatic merging approach for star schema-modeled data warehouses that works
at both the schema and instance levels. We also provide algorithms for merging
hierarchies, dimensions and facts. Eventually, we implement our merging
algorithms and validate them with the use of both synthetic and benchmark
datasets.
