Related papers: Guiding Data-Driven Design Ideation by Knowledge D…
The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc.…
Creative ideation relies on exploring diverse stimuli, but the overwhelming abundance of information often makes it difficult to identify valuable insights or reach the `aha' moment. Traditional methods for accessing design stimuli lack…
Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to…
Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features…
In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on…
Bisociative knowledge discovery is an approach that combines elements from two or more "incompatible" domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational…
Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation…
Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and techniques that support inspiration usually only focus on example browsing with limited user control or…
In order to create new products, inventors search and combine previous ideas. Few studies have examined the characteristics of search that lead to new products; most have focused on patent citations, which are often retrospective and may…
Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems…
Data-driven design and innovation is a process to reuse and provide valuable and useful information. However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information.…
We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale…
Patent data have long been used for engineering design research because of its large and expanding size, and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data…
Objective: Our study objective is to design a feasible technology solution for health organizations to remove barriers to evidence-based clinical information retrieval, and improve Evidence-Based Practice. Methods: Literature from 2010 to…
Concept discovery is one of the open problems in the interpretability literature that is important for bridging the gap between non-deep learning experts and model end-users. Among current formulations, concepts defines them by as a…
Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the…
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…
Discovery of new knowledge is increasingly data-driven, predicated on a team's ability to collaboratively create, find, analyze, retrieve, and share pertinent datasets over the duration of an investigation. This is especially true in the…
Scientific discovery is a cumulative process and requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging and critical challenge is how to identify conceptually relevant prior work from rapidly…
In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions…