Schema Inference for Multi-Model Data

The knowledge of a structural schema of data is a crucial aspect of most data management tasks. Unfortunately, in many real-world scenarios, the data is not accompanied by it, and schema-inference approaches need to be utilised. 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.