Name
Abstract | ||
|---|---|---|
There are a number of issues for information systems which are required to collect data urgently that are not well accommodated by current conceptual modelling methodologies and as a result the modelling step (and the use of databases) is often omitted. Such issues include the fact that •the number of instances for each entity are relatively low resulting in data definition taking a disproportionate amount of effort, •the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data, •they undergo regular structural change and are thus subject to information loss as a result of changes to the schema's information capacity, •finally, the structure of the information is likely to be 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 and to provide a platform and modelling technique to handle rapidly occurring phenomena. 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. LItER modelling also aims to facilitate 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. |
Year | Venue | Keywords |
|---|---|---|
2007 | ER (Tutorials, Posters, Panels & Industrial Contributions) | towards active conceptual modelling,two-part liter modelling process,modelling step,liter modelling,information system,information capacity,data definition,information loss,current conceptual modelling system,current conceptual modelling methodology,modelling technique,sudden event,conceptual schema,structural change,knowledge base |
Field | DocType | Citations |
SQL,Information system,Data mining,Ontology,Conceptual schema,Computer science,Data definition language,Knowledge base,Schema (psychology),Database,Conceptual model (computer science) | Conference | 1 |
PageRank | References | Authors |
0.37 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John F. Roddick | 1 | 1908 | 331.20 |
| Aaron Ceglar | 2 | 106 | 8.42 |
| Denise De Vries | 3 | 53 | 8.14 |