  A feature concept, the essence of the data-federative innovation process, is
presented as a model of the concept to be acquired from data. A feature concept
may be a simple feature, such as a single variable, but is more likely to be a
conceptual illustration of the abstract information to be obtained from the
data. For example, trees and clusters are feature concepts for decision tree
learning and clustering, respectively. Useful feature concepts for satis-fying
the requirements of users of data have been elicited so far via creative
communication among stakeholders in the market of data. In this short paper,
such a creative communication is reviewed, showing a couple of appli-cations,
for example, change explanation in markets and earthquakes, and highlight the
feature concepts elicited in these cases.
