Data Completeness and Complex Semantics in Conceptual Modeling: The Need for a Disaggregation Construct

Abstract

Conceptual modeling is important for developing databases that maintain the integrity and quality of stored information. However, classical conceptual models have often been assumed to work on well-maintained and high-quality data. With the advancement and expansion of data science, it is no longer the case. The need to model and store data has emerged for settings with lower data quality, which creates the need to update and augment conceptual models to represent lower-quality data. In this paper, we focus on the intersection between data completeness (an important aspect of data quality) and complex class semantics (where a complex class entity represents information that spans more than one simple class entity). We propose a new disaggregation construct to allow the modeling of incomplete information. We demonstrate the use of our disaggregation construct for diverse modeling problems and discuss the anomalies that could occur without this construct. We provide formal definitions and thorough comparisons between various types of complex constructs to guide future application and prove the unique interpretation of our newly proposed disaggregation construct.