Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning
Date
2019-10-16
Authors
Vázquez-Ingelmo, A.
García-Peñalvo, F. J.
Therón, R.
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
Information dashboards are sophisticated tools. Although they enable users to reach useful insights and support their decision-making challenges, a good design process is essential to obtain powerful tools. Users need to be part of these design processes, as they will be the consumers of the information displayed. But users are very diverse and can have different goals, beliefs, preferences, etc., and creating a new dashboard for each potential user is not viable. There exist several tools that allow users to configure their displays without requiring programming skills. However, users might not exactly know what they want to visualize or explore, also becoming the configuration process a tedious task. This research project aims to explore the automatic generation of user interfaces for supporting these decision-making processes. To tackle these challenges, a domain engineering, and machine learning approach is taken. The main goal is to automatize the design process of dashboards by learning from the context, including the end-users and the target data to be displayed.
Description
Keywords
Automatic generation, High-level requirements, Domain engineering, Meta-modeling, Information Dashboards
Citation
A. Vázquez-Ingelmo, F. J. García-Peñalvo and R. Therón, "Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning," in TEEM’19 Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (Leon, Spain, October 16th-18th, 2019), M. Á. Conde-González, F. J. Rodríguez-Sedano, C. Fernández-Llamas and F. J. García-Peñalvo, Eds. ICPS: ACM International Conference Proceedings Series, pp. 1007-1011, New York, NY, USA: ACM, 2019. doi: 10.1145/3362789.3362923.