Feature Concepts for Data Federative Innovations
This work addresses the challenge of interpreting data for users in federated settings, but it appears incremental as it reviews existing communication methods rather than proposing new solutions.
The paper tackles the problem of acquiring meaningful concepts from data by introducing 'feature concepts' as models for data-federative innovation, and reviews creative communication among stakeholders to elicit these concepts, with applications in markets and earthquakes.
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.