Critical Infrastructure Integration Modeling and Simulation

The protection of critical infrastructures, such as electrical power grids, has become a primary concern of many nation states in recent years. Critical infrastructures involve multi-dimensional, highly complex collections of technologies, processes, and people, and as such, are vulnerable to potentially catastrophic failures on many levels. Moreover, cross-infrastructure dependencies give rise to cascading and escalating failures across multiple infrastructures. In order to address the problem of critical infrastructure protection, this research is developing innovative approaches to modeling critical infrastructures, with emphasis on analyzing the ramifications of cross-infrastructure dependencies.

Critical Infrastructure Integration Modeling and Simulation
The protection of critical infrastructures, such as electrical power grids, has become a primary concern of many nation states in recent years. Critical infrastructures involve multi-dimensional, highly complex collections of technologies, processes, and people, and as such, are vulnerable to potentially catastrophic failures on many levels. Moreover, cross-infrastructure dependencies give rise to cascading and escalating failures across multiple infrastructures. In order to address the problem of critical infrastructure protection, this research is developing innovative approaches to modeling critical infrastructures, with emphasis on analyzing the ramifications of cross-infrastructure dependencies.

Combining Mobile Personalised Applications with Spatial Services

Within the next few years, the proliferation of advanced, GPS-enabled wireless devices will afford many opportunities for providing both recreational and professional users with a wide variety of location-aware services. This research is developing an advanced intelligent geo-spatial information architecture to provide users with highly-efficient, fine-grained, and personalised map information. Moreover, the project is developing an infrastructure for augmented reality, in which individual users can annotate spatial locations with a variety of media. Upon entering such an augmented location, relevant information, personalised to the user's interests within that space, can be accessed and presented. The work involves fundamental research in the areas of mobile computing, efficient storage and transmission of geo-spatial data, human-computer interaction, and personalization.

Combining Mobile Personalised Applications with Spatial Services
Within the next few years, the proliferation of advanced, GPS-enabled wireless devices will afford many opportunities for providing both recreational and professional users with a wide variety of location-aware services. This research is developing an advanced intelligent geo-spatial information architecture to provide users with highly-efficient, fine-grained, and personalised map information. Moreover, the project is developing an infrastructure for augmented reality, in which individual users can annotate spatial locations with a variety of media. Upon entering such an augmented location, relevant information, personalised to the user's interests within that space, can be accessed and presented. The work involves fundamental research in the areas of mobile computing, efficient storage and transmission of geo-spatial data, human-computer interaction, and personalization.

Sketch-Based Geo-Spatial Knowledge Management

Visual information management is a central concern for all industries that utilise photography in any guise. Geo-spatial information systems, in particular, are the foundation of a rapidly growing industry to deliver on-point geographic image data combined with meaningful associated information. They are used both as primary analysis tools, such as for intelligence information, and as value-added components, such as for route mapping. This research is developing a novel synthesis and new techniques for image information retrieval, geo-spatial databases, and knowledge management. The goal is to provide support for natural user sketch-based interaction, enhanced image matching, efficient geo-spatial database indexing and retrieval, and intelligent capture and re-use of task-based knowledge.

Sketch-Based Geo-Spatial Knowledge Management
Visual information management is a central concern for all industries that utilise photography in any guise. Geo-spatial information systems, in particular, are the foundation of a rapidly growing industry to deliver on-point geographic image data combined with meaningful associated information. They are used both as primary analysis tools, such as for intelligence information, and as value-added components, such as for route mapping. This research is developing a novel synthesis and new techniques for image information retrieval, geo-spatial databases, and knowledge management. The goal is to provide support for natural user sketch-based interaction, enhanced image matching, efficient geo-spatial database indexing and retrieval, and intelligent capture and re-use of task-based knowledge.

Data Mining Support for Collaborative Case-Based Reasoning

Case-based and collaborative filtering recommendation techniques have been, at the same time, viewed as both complimentary and contrasting. In the context of PTV, an applied recommender system operating in the TV listings domain, this project examines the potential benefits in merging the two techniques by developing CBR methods that employ collaborative filtering style ratings profiles directly as cases. Doing so presents a number of challenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering systems. This work expands on earlier CBR views of collaborative filtering, identifies problems and opportunities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge to improve recommendation services.

Data Mining Support for Collaborative Case-Based Reasoning
Case-based and collaborative filtering recommendation techniques have been, at the same time, viewed as both complimentary and contrasting. In the context of PTV, an applied recommender system operating in the TV listings domain, this project examines the potential benefits in merging the two techniques by developing CBR methods that employ collaborative filtering style ratings profiles directly as cases. Doing so presents a number of challenges, both in applying a case-based perspective to collaborative filtering, and in addressing the sparsity problem that plagues many collaborative filtering systems. This work expands on earlier CBR views of collaborative filtering, identifies problems and opportunities for similarity maintenance therein, and proposes and evaluates methods for mining and applying new similarity knowledge to improve recommendation services.

Information Management in Ubiquitous Computing

The increasing deployment of wireless information technology is exposing and affording more and more information to be incorporated into everyday life. People are already faced with fast increasing problems of information overload from wired electronic and online sources, and specialized information assistants will be required to manage the additional and ubiquitous information barrage. This project is developing techniques for flexibly extracting, analyzing, fusing, and presenting information from dynamic local communities of wireless devices under common contextual goals. The goal is to provide natural interaction with complex information environments.

David C. Wilson

Research