Fusion and mining spatial data in cyber-physical space with Dynamic Logic of Phenomena

Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The Neural Modeling Fields (NMF) theory and Dynamic Logic of Phenomena (DLP) address these challenges in a non-traditional way. The main idea behind their success is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process mimics processes of the mind and natural evolution at the neural level. This paper describes the generalization of DLP for data fusion and mining of heterogeneous spatial objects in cyber-physical space.