High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to un-trusted decision making downstream in the data analysis pipeline. We studied the problem of inconsistency detection and repair of the Ontology Multi-dimensional Data Model (OMD). We proposed 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 to consider the multiple criteria often needed in computing objective and accurate weights. Our presented methods are rooted in multi-criteria decision making that provide the benefits to consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain. Then we implemented minimal repair generation by pyDatalog and finally used genetic algorithm for scalability.

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