The category concept: an extension to the entity-relationship model
Data & Knowledge Engineering
Statistical and Scientific Database Issues
IEEE Transactions on Software Engineering
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
An ontological analysis of the relationship construct in conceptual modeling
ACM Transactions on Database Systems (TODS)
Software Engineering
Entity-Relationship Modeling: Foundations of Database Technology
Entity-Relationship Modeling: Foundations of Database Technology
COPLINK: managing law enforcement data and knowledge
Communications of the ACM
Handling Discovered Structure in Database Systems
IEEE Transactions on Knowledge and Data Engineering
Countering terrorism through information technology
Communications of the ACM - Homeland security
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Postponing schema definition: low instance-to-entity ratio (LItER) modelling
Active conceptual modeling of learning
The Strange Logic of Random Graphs
The Strange Logic of Random Graphs
Reduce, reuse, recycle: practical approaches to schema integration, evolution and versioning
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
Suggested research directions for a new frontier – active conceptual modeling
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
Postponing schema definition: low instance-to-entity ratio (LItER) modelling
Active conceptual modeling of learning
| Hi-index | 0.00 |
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.