Human-Centered Fusion Framework

Abstract

In recent years the benefits of fusing signatures extracted from large amounts of distributed and/or heterogeneous data sources have been largely documented in various problems ranging from biological protein function prediction to cyberspace monitoring. In spite of significant progress in information fusion research, there is still no formal theoretical framework for defining various types of information fusion systems, defining and analyzing relations among such types, and designing information fusion systems using a formal method approach. Consequently, fusion systems are often poorly understood, are less than optimal, and/or do not suit user needs. To start addressing these issues, we outline a formal human-centered fusion framework for reasoning about fusion strategies. Our approach relies on a new taxonomy for fusion strategies, an alternative definition of information fusion in terms of parameterized paths in signature related spaces, an algorithmic formalization of fusion strategies and a library of numeric and dynamic visual tools measuring the impact as well as the impact behavior of fusion strategies. Using a real case of intelligence analysis we demonstrate that the proposed framework enables end users to rapidly 1) develop and implement alternative fusion strategies, 2) understand the impact of each strategy, 3) compare the various strategies, and 4) perform the above steps without having to know the mathematical foundations of the framework. We also demonstrate that the human impact on a fusion system is critical in the sense that small changes in strategies do not necessarily correspond to small changes in results.