Structural Inferences from Massive Datasets

High-level understanding of data must involve the interplay between substantial prior knowledge with geometric and statistical techniques . Our approach emphasizes the recovery of basic structural elements and their interaction patterns in order to summarize and draw inferences about the significant features contained in the data . As a testbed for modeling how scientists analyze and extract knowledge of structure morphogenesis from data, we examine the datasets obtained from numerical simulation of turbulence . We describe a program that automatically extracts 3D structures, classifies them geometrically, and analyzes their spatial and temporal coherence . Our program is constructed by mixing and matching the aggregate, classify,-and re-describe operators of the spatial aggregation language . The research is a continuation of the effort to investigate the role of imagistic reasoning in human thinking .