This program aims to develop conceptual models for complex systems and to explore how
these models can be used to build data-oriented solutions to difficult problems.
For example, while current conceptual modelling techniques have proved adequate for many problems, they cannot
adequately model many advanced semantic relationships characteristic of complex systems.
Our approach is to retain the overall structure of the relational, EER, ORM and other models, and to extend
then by adding the ability to handle more advanced semantics.
In particular, we are exploring techniques that support schema evolution, temporal semantics,
data summarization and techniques applicable for distributed and mobile databases.
For example, one recent focus for the group has been in the development of a model capable of modelling systems for which the number of instances for each entity is relatively low, where the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data, systems that undergo regular structural change and are thus subject to information loss as a result of changes to the schema's information capacity, and 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.
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.
Another focus has been on the development of mesodata theory. This is an extension to the relational model to allow for the design and development of complex domains in an intuitive manner.