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.

中文翻译：

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数据驱动的类比设计：最新技术和未来方向

类比设计 (DbA) 是一种设计方法，其中基于从源域中汲取的灵感，在目标域中生成新的解决方案、机会或设计；它可以使设计师在减轻设计固定和改善设计构思结果方面受益。最近，越来越多的设计数据库和快速发展的数据科学和人工智能技术为开发数据驱动的方法和工具提供了新的机会，以支持 DbA。在这项研究中，我们调查了现有的数据驱动的 DbA 研究，并根据数据、方法和应用将个别研究分为四类，即类比编码、检索、映射和评估。基于细致入微的有机审查和结构化分析，本文阐明了迄今为止数据驱动的 DbA 研究的最新技术水平，并将其与数据科学和人工智能研究的前沿相结合，以确定该领域有前景的研究机会和方向。最后，我们提出了一个集成所有命题的未来概念数据驱动的 DbA 系统。