Assessing the quality of information system schemas is crucial, because an unoptimized or erroneous schema design has a strong impact on the quality of the stored data, e.g., it may lead to inconsistencies and anomalies at the data-level. Even if the initial schema had an ideal design, changes during the life cycle can negatively affect the schema quality and have to be tackled. Especially in Big Data environments there are two major challenges: large schemas, where manual verification of schema and data quality is very arduous, and the integration of heterogeneous schemas from different data models, whose quality cannot be compared directly. Thus, we present a domain-independent approach for automatically measuring the quality of large and heterogeneous (logical) schemas. In contrast to existing approaches, we provide a fully automatable workflow that also enables regular reassessment. Our implementation allows to measure the quality dimensions correctness, completeness, pertinence, minimality, readability, and normalization.
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