Data and
Knowledge Management
This research group has interests in all aspects of
developing and managing large quantities of data. One of the major problems at
the moment with information systems is that of interoperability: the difficulty
in bringing data together from different information sources. Such problems
arise even when the different knowledge bases employ the same data model but
are compounded in heterogeneous systems.
A multi-level architecture with
meta- and meta-meta mappings has been suggested as a solution to these
problems, perhaps using tools such as the semantic Web, RDF (Resource
Description Framework), XML (eXtensible Markup Language), the Grid and MOF
(Meta Object Facility) or using a theoretical approach such as category theory.
Other areas of interest include:
·
Developing an architecture,
design and implementation for a pure relational database system to handle
objects in a simple manner
·
Data mining
·
Semi-structured data
modelling, including handling of XML
·
Version management in
databases
·
Theoretical basis for
object databases, ideally using category theory
·
Natural computing including
quantum databases and techniques, anticipatory systems and physical process.