Iván Ordóñez, The Ohio State University; Feng Zhao, Xerox Palo Alto Research Center
Spatio-temporal data sets arise when time-varying physical fields are discretized for simulation or analysis. Examples of time-varying fields are isothermal regions in the sea, or pattern formations in natural systems, such as convection rolls or diffusion-reaction systems. The analysis of these data sets is essential to generate qualitative interpretations for human understanding. This paper presents Spatio-Temporal Aggregation (STA), a system for recognizing and tracking qualitative structures in spatio-temporal data sets. STA algorithms record and maintain temporal events and compile event se-quences into concise history descriptions. This is carried out at several levels of description, from the bottom up: first, low level events are identified and tracked, and then a subset of those events, relevant at the next description level, is identified. The process is iterated until a high level narrative of the system’s temporal evolution is obtained. STA has been demonstrated on a class of diffusion-reaction systems in two dimensions and has successfully generated high-level symbolic descriptions of systems similar to those produced by scientists through carefully hand-tuned computational experiments.