Abstract
There are four classes of information system that are not well served by current modelling techniques. First, there are systems for which the number of instances for each entity is relatively low resulting in data definition taking a disproportionate amount of effort. Second, there are systems where the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data. Third, there are those that undergo regular structural change and are thus subject to information loss as a result of changes to the schema’s information capacity. Finally, there are those systems where the structure of the information is only partially known or for which there are multiple, perhaps contradictory, competing hypotheses as to the underlying structure.
This paper presents the Low Instance-to-Entity Ratio (LItER) Model, which attempts to circumvent some of the problems encountered by these types of application. The two-part LItER modelling process possesses an overarching architecture which provides hypothesis, knowledge base and ontology support together with a common conceptual schema. This allows data to be stored immediately and for a more refined conceptual schema to be developed later. It also facilitates later translation to EER, ORM and UML models and the use of (a form of) SQL. Moreover, an additional benefit of the model is that it provides a partial solution to a number of outstanding issues in current conceptual modelling systems.
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Roddick, J.F., Ceglar, A., de Vries, D., La-Ongsri, S. (2007). Postponing Schema Definition: Low Instance-to-Entity Ratio (LItER) Modelling. In: Chen, P.P., Wong, L.Y. (eds) Active Conceptual Modeling of Learning. ACM-L 2006. Lecture Notes in Computer Science, vol 4512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77503-4_16
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DOI: https://doi.org/10.1007/978-3-540-77503-4_16
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