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Schema Inference for Multi-Model Data

Publication at Faculty of Mathematics and Physics |
2022

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.