Merging Multidimensional Data Models: A Practical Approach for Schema and Data Instances
Michael Mireku Kwakye, Iluju Kiringa, Herna Lydia Viktor · Databases, Knowledge, and Data Applications · 2013
Meta-model merging is the process of incorporating data models into an integrated, consistent model against which accurate queries may be processed. Within the data warehousing domain, the integration of data marts is often time-consuming. In this paper, we introduce an approach for the integration of relational star schemas, which are instances of multidimensional data models. These instance schemas represented as data marts are integrated into a single consolidated data warehouse. Our methodology which is based on model management operations focuses on a formulated merge algorithm and adopts first-order Global-and-Local-As- View (GLAV) mapping models, to deliver a polynomial time, near-optimal solution of a single integrated data warehouse. Keywords-Schema Merging; Data Integration; Model Management; Multidimensional Merge Algorithm; Data Warehousing