Data profiling : efficient discovery of dependencies Data profiling is the computer science discipline of analyzing a given dataset for its metadata. The types of metadata range from basic statistics such as tuple counts column aggregations and value distributions to much more complex structures in particular inclusion dependencies (INDs) unique column combinations (UCCs) and functional dependencies (FDs). If present these statistics and structures serve to efficiently store query change and understand the data. Most datasets however do not provide their metadata explicitly so that data scientists need to profile them. While basic statistics are relatively easy to calculate more complex structures present difficult mostly NP complete discovery tasks; even with good domain knowledge it is hardly possible to detect them manually. Therefore various profiling algorithms have been developed to automate the discovery. None of them however can process datasets of typical real world size because their resource consumptions and or execution times exceed effective limits .…