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My publications are available from the Computer Science department publications repository.
My research interests encompass these interlinked areas:
Semantics, knowledge representation and interoperabilityThis strand of the research builds upon previous work on ontological representation and multidimensional information management. I am interested in exploring the use of domain knowledge to represent the notion of data spaces over heterogeneous information stores. The research also explores the application of knowledge-based techniques to developing a computational infrastructure for spatiotemporal decision support.
This research is part of the Interreg III interdisciplinary project CHARM. Further information and details about CHARM marine interoperability research are available here.
Principles of semantic information representation for knowledge discovery were investigated in the Interreg II project Biodiversity and cartography of marine resources in the Dover Strait. Of particular interest is the use of semantics and metadata for multivariate information to provide insights into factors determining environmental processes.
The FishCAM research project was funded by the European Union under the CRAFT initiative as part of the thematic programmes 'Sustainable Agriculture, Fisheries and Forestry' and 'Sustainable Marine Ecosystems'.
The challenge was to develop modular software for the EU fishing industry. For an overview of the three main packages in FISHCAM, the onboard module (electronic logbook), spatio-temporal analysis and trajectory mapping, click here.
Geolibraries: Many scientific domains generate vast volumes of heterogeneous geo-referenced data. I am interested in the data management requirements of efficient infrastructures in these domains. The focus of STepLIB (Spatio-Temporal digital LIBrary) was to bridge the 'structure chasm' to enable management, indexing and searching complex data types. Here is a link to publications relating to the STepLIB digital library research for geospatial and multimedia data.
Hierarchical reasoning: phenomena often exhibit different characteristics depending on the scale of the observations. Hierarchical reasoning enables the understanding of scale and categorization effects in data analysis. I am particularly interested in the computational support required for reasoning about data in multiple dimensions and at various levels. Click here for publications related to hierarchical reasoning in space and time.