Models of information : the feasibility of measuring the stability of data models
The theory of data modelling makes a variety of claims about schema stability. This research determined the current state of data modelling practice, and tested hypotheses related to measuring model stability. The research developed a method whereby the major elements of a data model can be consistently represented whatever process was originally used for modelling. This was achieved through a construction of a logical relational schema from the record design. The construction/reconstruction process attempted to identify the primary meaning primitives of a data model in order to track changes to them in different iterations of the application. The stability data collection process was applied to a case study followed by a series of models to generate further data. The early evidence indicated that data model instability has it roots in errors in modelling, errors in the semantic analysis whether done consciously or intuitively, and in changes to the requirements brought on by changes to the "reality". This research suggested that some of the elements of a data model are significantly more important than others. The research documented problems associated with the transformation of natural language into the constraints of data dictionaries. This exploration into the potential application of linguistic research into systems theory and practice identified a number of theoretically interesting problems, such as variable semantic determination. The discussion outlined some specific techniques an analyst can use to improve the process of semantic analysis. The work suggested that there should be greater concentration on the question of data model evolvability, and the appropriate preservation of meaning across model versions, and not necessarily on data model stability.