Abstract: In some implementations, a system may receive inventory data associated with a data storage system. The inventory data identifies file paths for objects stored in the data storage system. The system may detect patterns in prefixes of the file paths using one or more trained machine learning models. The system may normalize the prefixes of the file paths based on the patterns detected in the prefixes. The system may detect datasets of the objects stored in the data storage system based on the normalized prefixes. The system may compare prefixes associated with the detected datasets with prefixes associated with a set of registered datasets that are registered with a metadata repository. The system may determine, based on comparing the prefixes associated with the detected datasets and the prefixes associated with the set of registered datasets, a respective registration classification for each detected dataset.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Abstract: A system, method, and computer-readable medium for generating factual and/or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and/or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.