Restoring Consistency in Ontological Multidimensional Data Models via Weighted Repairs

High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this paper, we study the problem of inconsistency detection and repair of the Ontology Multi-dimensional Data Model (OMD). We propose a framework of data quality assessment, and repair for the OMD. We formally define a weight-based repair-by-deletion semantics, and present an automatic weight generation mechanism that considers multiple input criteria. Our methods are rooted in multi-criteria decision making that consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain.