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Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning

Published: 16 October 2019 Publication History

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.

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Cited By

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  • (2023) Analyzing the color availability of AI ‐generated posters based on K ‐means clustering: 74% orange, 38% cyan, 32% yellow, and 28% blue‐cyan Color Research & Application10.1002/col.2291249:2(234-257)Online publication date: 27-Nov-2023
  • (2020)Advances in the use of domain engineering to support feature identification and generation of information visualizationsEighth International Conference on Technological Ecosystems for Enhancing Multiculturality10.1145/3434780.3436640(1053-1056)Online publication date: 21-Oct-2020

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      TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality
      October 2019
      1085 pages
      ISBN:9781450371919
      DOI:10.1145/3362789
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      • University of Salamanca: University of Salamanca

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      New York, NY, United States

      Publication History

      Published: 16 October 2019

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      Author Tags

      1. Automatic generation
      2. Domain engineering
      3. High-level requirements
      4. Information Dashboards
      5. Meta-modeling

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      View all
      • (2023) Analyzing the color availability of AI ‐generated posters based on K ‐means clustering: 74% orange, 38% cyan, 32% yellow, and 28% blue‐cyan Color Research & Application10.1002/col.2291249:2(234-257)Online publication date: 27-Nov-2023
      • (2020)Advances in the use of domain engineering to support feature identification and generation of information visualizationsEighth International Conference on Technological Ecosystems for Enhancing Multiculturality10.1145/3434780.3436640(1053-1056)Online publication date: 21-Oct-2020

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