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Information Integration

In this new age of information technology, an increasing number of highly complex software systems and artifacts are taking center stage in the world of technology and society at large. Some examples of such systems or artifacts are autonomous unmanned aerial vehicles and automobiles, command and control systems operating in network-centric contexts, or multi-modal interfaces for human interaction with such systems.

One common characteristic of such systems is the huge flow of raw data through the system, often from distributed sources, and the requirement to fuse the data in different ways to meet the needs of complex components which make up such systems. Many of the artifacts targeted for research in the center, such as unmanned aerial vehicles, require on-line support for sensor and information fusion capability at various levels of abstraction. Two examples of components which require such information fusion and integration might be a collision avoidance system for an automobile or UAV, or a GIS for the environment in which an automobile or UAV is operating. These two example are at far ends of the information spectrum, yet one can easily imagine the need for combining data from navigational sensors with information from a GIS in order to develop sophisticated navigational components in highly autonomous systems such as UAVs.

Traditionally, there have been different approaches used in the system engineering and computer science communities to deal with sensor fusion and information integration, where the former employ probabilistic frameworks using dynamic programming and Bayes Theorem as a basis, while the latter focus on qualitative techniques associated with model generation and reasoning with such models. In addition, due to the highly distributed nature of the information sources in such systems there is a move towards the use of the agent paradigm as a means of controlling complexity in such systems.

One of the main research topics in the center, which we name "information integration", will be to leverage competences in computer science, artificial intelligence and systems engineering and signal processing, in the development of complex information fusion and integration components for highly complex systems and autonomous artifacts.