Design de interação e mineração de dados : revisão sistemática e desdobramentos futuros

Dissertação (mestrado)—Universidade de Brasília, Instituto de Artes, Programa de Pós-Graduação em Design, 2019.Com a emergência da geração, coleta e análise de grande quantidade de dados de uso em diversas disciplinas, esta pesquisa busca sintetizar as aplicações já utilizadas da mineração de dados no processo de design de interação. Busca-se entender os esforços já empreendidos, as lacunas existentes e as possibilidades futuras para a interseção dessas duas disciplinas; é discutida ainda a terminologia a ser utilizada para se referir a esse junção: data-aware design. A metodologia utilizada foi a revisão sistemática, utilizando como base Costa e Zoltowski (2014) e Schiavon (2015). Os referenciais teóricos para o design de interação foram Norman (2013) e Verplank (2009), enquanto o referencial para a mineração de dados foi Han et al. (2012). Foram analisados nove estudos, todos em língua inglesa, que apresentam empolgação com as possibilidades que a mineração de dados pode trazer ao design de interação, mas apenas quatro apresentaram dados, mesmo que incompletos, para demonstrar os benefícios dessa abordagem, e apenas dois manifestaram preocupações com a privacidade e segurança no uso de dados dos usuários.With the emergence of the generation, collection, and analysis of a great amount of usage data in different fields, this research looks to synthesize the data mining activities that happened in the interaction design process. This research strives to understand the efforts made, the existing gaps and the future possibilities for the intersection of these two fields. It also discusses how to call this intersection: dataaware design. The methodology used was the systematic review, based on Costa and Zoltowski (2014) and Schiavon (2015). The authors referenced for interaction design were Norman (2013) and Verplank (2009), while Han et al. (2012) were referenced for data mining. Nine studies were analyzed, all written in English. Overall they show excitement towards what data mining can bring to interaction design, but only four show data, even if incomplete, to demonstrate the benefits of this approach, and only two studies showed concerns regarding privacy and security while using user data

Multi-indicators decision for product design solutions: a TOPSIS-MOGA integrated model

Design decisions occur in all phases of product design and largely affect the merits of the final solution, which will ultimately determine the success or failure of the product in the market. Product design is a continuous process, and a large number of existing studies have proposed decision methods and decision indicators for the characteristics of different stages of design. These methods and indicators can meet the requirements of one of the phases: demand analysis, conceptual design, or detailed design. However, further research can still be conducted on the integration of methods throughout the design phase, using intelligent design methods, and improving the design continuity and efficiency. To address this problem, a TOPSIS-MOGA-based multi-indicators decision model for product design solutions is proposed, including its product design process, decision algorithm, and selection method. First, a TOPSIS-MOGA integrated model for conceptual design and detailed design process is established, the continuity of decision-making methods is achieved by integrating decision indicators. Second, conceptual design solutions are selected through the technique for order of preference by similarity to ideal solution (TOPSIS), based on hesitant fuzzy linguistic term sets and entropy weight method. Finally, detailed design solutions are selected through a multiobjective genetic algorithm (MOGA), based on a polynomial-based response surface model and central combination experimental design method. A case study of the decision-making in the design of high-voltage electric power fittings is presented, the conceptual design phase and the detailed design phase are connected through the indicators, which demonstrates that the proposed approach is helpful in the decision-making of the product design solutions