Dynamic identification by enumeration for co-operative knowledge discovery

In contemporary information and communication technologies, there is an urgent need for transforming tools into assistant systems. Humans do not need more digital tools that require learning how to wield them, but digital assistants guiding them to unforeseeably valuable results – an effect named serendipity. This applies particularly when dealing with wicked problems which change over time when being tackled. Data analysis, visualization, and exploration is a characteristic domain of this type, particularly when open data are in focus, because the analysts have no background knowledge about the origin of these open data. The paper demonstrates the transformation of a tool for data analysis into an intelligent adaptive assistant. The transformation is based on the exploitation of concepts, methods, and technologies from disciplines such as meme media technology, natural language processing, and theory of mind modeling and induction. In comparison to earlier approaches to computational theory of mind induction, the present one relies on dynamically generated spaces of hypotheses. A rigorous mathematical proof demonstrates the superiority of the novel reasoning technology. A case study in business intelligence serves as proof of concept.