Abstract
In this paper, we focus on a specific and complex use case of multi-model data where several often contradictory features of the combined models must be considered. Hence, single-model approaches cannot be applied straightforwardly.
In addition, the data often reach the scale of Big Data, and thus a scalable solution is inevitable. In our approach, we reflect all these challenges.
In addition, we can also infer local integrity constraints as well as intra- and inter-model references. Last but not least, we can cope with cross-model data redundancy.
Using a set of experiments, we prove the advantages of the proposed approach and we compare it with related work.