Characterizing Information Modeling Techniques

Information modeling is concerned with the construction of symbolic structures which capture the meaning of information and organize it in ways that make it understandable and useful to people. Given that information is becoming a ubiquitous, abundant and precious resource, information modeling is serving as a core technology for information systems engineering. We present a brief history of information modeling techniques in Computer Science and survey such techniques developed within Knowledge Representation (Artificial Intelligence), Data Modeling (Databases), and Requirements Analysis (Software Engineering and Information Systems). The presentation then offers a comparative framework for information modeling proposals which classifies them according to their ontologies, i.e., the type of application for which they are intended, the set of abstraction mechanisms (or, structuring principles) they support, as well as the tools they provide for building, analyzing, and managing application models. Examples of ontologies include static worlds consisting of entities and relationships, or dynamic ones consisting of processes. Generalization, aggregation, and classification are three of the best known abstraction mechanisms, adopted by many information models and used widely in information modeling practice. The final component of the paper uses the comparative framework proposed earlier to assess well known information modeling techniques, both from a user and a designer perspective.