Improvement of usability in user interfaces for massive data analysis: an empirical study
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Big Data challenges the conventional way of analyzing massive data and creates the need to improve the usability of existing user interfaces (UIs) in order to deal with massive amounts of data. How the UIs facilitate the search for information and helps in the end-user’s decision-making depends on developers and designers, who have no guides for producing usable UIs. We have proposed a set of interaction patterns for designing massive data analysis UIs by studying 27 real case studies of massive data analysis. We evaluate if the proposed patterns improve the usability of the massive data analysis UIs in the context of literature search. We conducted two replications of the same controlled experiment, one with 24 undergraduate students experienced in scientific literature search and the other with eight researchers who are experienced in biomedical literature search. The experiment, which was planned as a repeated measures design, compares UIs that have been enhanced with the proposed patterns versus original UIs in terms of three response variables: effectiveness, efficiency, and satisfaction. The outcomes show that the use of interaction patterns in UIs for massive data analysis yields better and more significant effects for the three response variables, enhancing the discovery and visualization of the data. The use of the proposed interaction design patterns improves the usability of the UIs that deal with massive data. The patterns can be considered as guides for helping designers and developers to design usable UIs for massive data analysis web applications.
KeywordsUser interface Big data Usability Interaction design patterns
The authors thank the members of the PROS Center Genome group for productive discussions. In addition, it is also important to highlight that the Secretaría Nacional de Educación, Ciencia y Tecnología (SENESCYT) and the Escuela Politécnica Nacional from Ecuador have supported this work. This project has also been developed with the financial support of the Spanish State Research Agency and the Generalitat Valenciana, under the projects TIN2016-80811-P and PROMETEO/2018/176, and co-financed with ERDF.
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