Feature Concepts for Data Federative Innovations

TL;DR

This paper introduces the concept of feature concepts as a model for data-federative innovations, illustrating their role in understanding data and supporting applications like market change explanation and earthquake analysis.

Contribution

It presents a new model of feature concepts for data-federative innovation and reviews their elicitation through creative stakeholder communication in various applications.

Findings

Feature concepts serve as abstract representations of data insights.

Applications include change explanation in markets and earthquakes.

Creative communication among stakeholders is key to eliciting useful feature concepts.

Abstract

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.