Leveraging Scalable Profiling to Learn and Visualize the Latest Trustworthy COVID-19 Medical Research Findings

Michael N. Gubanov, Sophie Pavia, Anna Pyayt, William Goble

Abstract: Accessing large-scale structured datasets such as WDC [31] or CORD-191 is very challenging [ 11, 13, 14, 41, 42]. Even if one topic (e.g. Vaccine Side-Effects) is of interest, the side-effects tables in different papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate our scalable Meta-data profiler, capable of constructing a standardized interface to a topic of interest in large-scale structured datasets. This interface, called Meta-profile represents a meta-data summary per each topic, representative of the entire dataset. Such profiles can be used as a robust visualization as well as to simplify access to structured data for both data scientists and end users at scale [32, 42]