  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.
