Data Crystallization for Discovering Unobservable Items

摘要

It is only the observable part of the real world that can be stored in data. For such a scattered, i.e., an incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable ones. This is realized with a tool which insert dummy items, corresponding to unobservable events, to the given data on past events. The existence of these unobservable events and their relations with other events are visualized by applying KeyGraph iteratively to the data donated with dummy items, gradually increasing the number of edges in the graph, like the crystallization of snow with gradual decrease in the air temperature. For tuning the granularity level of structure to be visualized, this tool is integrated with human's process of chance discovery. This basic method is expected to be applicable for various real world domains where chance-discovery methods have been applied.