Data & Knowledge Management
Due to the highly distributed nature of modern
information systems, data management has reached another advanced level. The
relational data sets no longer support a variety of requirements, and often deal
with less structured data and purposefully tailored data types. As a result, XML
and its integration with relational databases has become a frequent necessity.
EII is in a very strong position to contribute to this research program through
collaboration with experts in inter-organisational computing, and cultivating
industry links to bring additional insights to established expertise in the
field.
Relational databases are often employed in order to store the content of a web
site. At the same time, XML is fast emerging as the dominant standard for data
interchange and publication. Thus, the integration of XML documents with
relational database systems to enable the storage, retrieval, and update of XML
documents is of major importance. Data model heterogeneity and schema
heterogeneity, however, make this a challenging task. In this research, we
investigate approaches for integrating XML documents, and mapping between XML
documents/databases and relational databases, in order to build up applications
that use the integrated documents/resources. Other issues that must be
investigated include: formal approaches for XML document/database design,
various constraints specification and enforcement, query processing, and
updating methods and optimisation.
Knowledge management (KM) is defined as "a multi-disciplinary approach to
achieving organisational objectives by making the best use of knowledge. It
involves the design, review and implementation of both social and technological
processes to improve the application of knowledge, in the collective interest of
stakeholders" (Standards Australia International, 2003).
The nature of many KM systems is pervasive with an enterprise wide or community
wide scope. The intelligent activities of organisations and communities are
increasingly mediated and recorded in digitised form, providing histories of
useful records which are relevant to smarter decision making and for enacting
informed choices. Also, as we increasingly share our social and living
environment with intelligent and (semi) autonomous devices and agents, the is
critical to ensure that the technological and human aspects of knowledge are
aligned.
Program Coordinator
Prof.
Xuemin Lin
is a Professor in the School of Computer Science and Engineering at the
University of New South Wales. His principal research areas are database systems
and graph visualisation with the goal is to develop efficient and effective
techniques that are fundamental to modern applications of database systems.
Currently, he is working on data streams, approximate query processing, and
spatial query processing.
