Data-Driven Design-by-Analogy: State of the Art and Future Directions

TL;DR

This paper surveys the current state of data-driven Design-by-Analogy, categorizing research by data, methods, and applications, and proposes future directions for integrating AI and data science into DbA.

Contribution

It provides a comprehensive review and structured analysis of data-driven DbA research, highlighting gaps and proposing a conceptual integrated system for future development.

Findings

Categorized DbA studies into analogy encoding, retrieval, mapping, and evaluation.

Identified promising research opportunities at the intersection of AI and DbA.

Proposed a future integrated data-driven DbA system.

Abstract

Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.