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AI Applied to Data Visualization
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AI Applied to Data Visualization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 18895

Special Issue Editors

Dr. Anna Puig

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Avda Corts Catalanes, 585, 08007 Barcelona, Spain
Interests: data visualization; computer graphics; serious games; AI-based interactions
Dr. Inmaculada Rodriguez

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Avda Corts Catalanes, 585, 08007 Barcelona, Spain
Interests: data visualization; HCI; serious games; virtual reality; AI-based interactions

Special Issue Information

Dear Colleagues,

Data visualization has always been fundamental to enhance human cognition. However, the data to be processed and comprehended is now more massive and complex than ever. Moreover, finding the best data visualization depends not only on the data and their possible graphical representations, but also on the end-users' expectations and their intended interactions. The application of AI to data visualization is an emerging area that may help to create and enhance visualizations, facilitate user interactions, and aid visualization analysis.

The aim of current AI-based data visualizations is to facilitate tasks related to data such as transformations, assessment, mining, comparison, querying, recommendation, reasoning, human interaction, and inmersiveness.

This Special Issue is dedicated to new approaches and perspectives of the application of AI to data visualization and multidisciplinary research areas.

Topics of interest include, but are not limited to:

  • Theory and models;
  • Visualization generation;
  • Visualization enhancement;
  • Visualization analysis;
  • Adaptive visualizations;
  • Intelligent human–data interaction;
  • Multimodal visualizations;
  • Natural language interfaces for data visualization;
  • Data visualization in immersive environment applications.

Dr. Anna Puig
Dr. Inmaculada Rodriguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data visualization
  • artificial intelligence
  • AI-based interactions
  • AI-driven visualizations
  • NLP in data visualization

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Published Papers (6 papers)

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Research

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18 pages, 2224 KiB  
Article
Guided Decision Tree: A Tool to Interactively Create Decision Trees Through Visualization of Subsequent LDA Diagrams
by Miguel A. Mohedano-Munoz, Laura Raya and Alberto Sanchez
Appl. Sci. 2024, 14(22), 10497; https://doi.org/10.3390/app142210497 - 14 Nov 2024
Viewed by 481
Abstract
Decision trees are a widely used machine learning technique due to their ease of interpretation and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which [...] Read more.
Decision trees are a widely used machine learning technique due to their ease of interpretation and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher’s criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes’ information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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14 pages, 5300 KiB  
Article
A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation
by Chng Wei Lau, Daniel Catchpoole, Simeon Simoff, Dongmo Zhang and Quang Vinh Nguyen
Appl. Sci. 2023, 13(18), 10178; https://doi.org/10.3390/app131810178 - 10 Sep 2023
Cited by 1 | Viewed by 2134
Abstract
Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms [...] Read more.
Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms and visualisation. However, most of the existing works do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. This paper contributes a novel strategy that applies game theory within a VR-enabled immersive visualisation system designed as the decision support engine to mimic real-world interactions between stakeholders within complex relationships, in this case cancer clinicians. Our focus is to apply game theory to assist doctors in the decision-making process regarding the treatment options for rare-cancer patients. Nash Equilibrium and Social Optimality strategy profiles were used to facilitate complex analysis within the visualisation by inspecting which combination of genes and dimensionality reduction methods yields the best survival rate and by investigating the treatment protocol to form new hypotheses. Using a case simulation, we demonstrate the effectiveness of game theory in guiding the analyst with a patient cohort data interrogation system as compared to an analyst without a decision support system. Particularly, the strategy profile (t-SNE method and DNMT3B_ZBTB46_LAPTM4B gene) gains the highest payoff for the two doctors. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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20 pages, 2949 KiB  
Article
Improving Dimensionality Reduction Projections for Data Visualization
by Bardia Rafieian, Pedro Hermosilla and Pere-Pau Vázquez
Appl. Sci. 2023, 13(17), 9967; https://doi.org/10.3390/app13179967 - 4 Sep 2023
Cited by 3 | Viewed by 3940
Abstract
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim of preserving significant properties from the original data. Many dimensionality [...] Read more.
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim of preserving significant properties from the original data. Many dimensionality reduction algorithms exist, and nonlinear approaches such as the t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) have gained popularity in the field of information visualization. In this paper, we introduce a simple yet powerful manipulation for vector datasets that modifies their values based on weight frequencies. This technique significantly improves the results of the dimensionality reduction algorithms across various scenarios. To demonstrate the efficacy of our methodology, we conduct an analysis on a collection of well-known labeled datasets. The results demonstrate improved clustering performance when attempting to classify the data in the reduced space. Our proposal presents a comprehensive and adaptable approach to enhance the outcomes of dimensionality reduction for visual data exploration. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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19 pages, 9165 KiB  
Article
Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
by Junkang An, Yiwan Zhang and Inwhee Joe
Appl. Sci. 2023, 13(15), 8782; https://doi.org/10.3390/app13158782 - 29 Jul 2023
Cited by 8 | Viewed by 2698
Abstract
Deep learning researchers believe that as deep learning models evolve, they can perform well on many tasks. However, the complex parameters of deep learning models make it difficult for users to understand how deep learning models make predictions. In this paper, we propose [...] Read more.
Deep learning researchers believe that as deep learning models evolve, they can perform well on many tasks. However, the complex parameters of deep learning models make it difficult for users to understand how deep learning models make predictions. In this paper, we propose the specific-input local interpretable model-agnostic explanations (LIME) model, a novel interpretable artificial intelligence (XAI) method that interprets deep learning models of tabular data. The specific-input process uses feature importance and partial dependency plots (PDPs) to select the “what” and “how”. In our experiments, we first obtain a basic interpretation of the data by simulating user behaviour. Second, we use our approach to understand “which” features deep learning models focus on and how these features affect the model’s predictions. From the experimental results, we find that this approach improves the stability of LIME interpretations, compensates for the problem of LIME only focusing on local interpretations, and achieves a balance between global and local interpretations. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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17 pages, 1966 KiB  
Article
TrendFlow: A Machine Learning Framework for Research Trend Analysis
by Tao Xiang, Sufang Chen, Yiwei Zhang and Rui Zhu
Appl. Sci. 2023, 13(12), 7029; https://doi.org/10.3390/app13127029 - 11 Jun 2023
Cited by 1 | Viewed by 2981
Abstract
As various research fields continue to evolve, new technologies emerge constantly, making it challenging for scholars to keep up with the latest and most promising research directions. To address this issue, we propose TrendFlow, a framework that leverages machine learning and deep learning [...] Read more.
As various research fields continue to evolve, new technologies emerge constantly, making it challenging for scholars to keep up with the latest and most promising research directions. To address this issue, we propose TrendFlow, a framework that leverages machine learning and deep learning techniques for analyzing research trends. TrendFlow first searches relevant literature based on user-defined queries, then clusters the searched literature according to the abstracts, and finally generates keyphrases of the abstracts as research trends for each cluster. Our experimental results highlight the superior performance of TrendFlow compared to traditional literature analysis tools. We have released the beta version of TrendFlow on Huggingface. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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Review

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34 pages, 9779 KiB  
Review
Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
by Ecem Kavaz, Anna Puig and Inmaculada Rodríguez
Appl. Sci. 2023, 13(12), 7025; https://doi.org/10.3390/app13127025 - 11 Jun 2023
Cited by 6 | Viewed by 4866
Abstract
Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. [...] Read more.
Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods. Full article
(This article belongs to the Special Issue AI Applied to Data Visualization)
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