Understanding Semantic Completeness in Rule Frameworks for Modeling Cardinality Constraints

Abstract

Modeling organizational rules during conceptual design provides a more accurate picture of the underlying domain and helps enforce data integrity. In a database development context, there are many advantages to explicitly representing rules during conceptual design. Early modeling ensures they are visible to designers and users, thus improving requirements and validation. The rules can then be semiautomatically translated into logical design code. One limitation to widespread adoption of such modeling is variance in standards and semantics of rules. We consider cardinality constraints—a useful and integral part of conceptual database design. Many papers discussing classification frameworks for cardinality exist. Completeness of such schemes has always been in question since well-defined criteria do not exist to evaluate them. We suggest a “reverse engineering” approach, i. e., one of defining conceptual modeling constraint completeness based on mappings from the relational model. We develop a correspondence from relational algebra operator combinations to existing semantic constraint types. In doing so, we also come up with a new category of set-level cardinality constraints not previously examined in literature. We believe our work demonstrates a unique approach to establishing conceptual framework completeness and enables standardization of rule semantics which in turn allows for semantics-based (as opposed to procedural-based) representation. On the implementation side, it supports developing automated mechanisms for translating constraints to improve developer productivity.