Interactive Visualizations: Design, Technologies and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3193

Special Issue Editor


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Guest Editor
Section Industrial Engineering and Business Information Systems (IEBIS), University of Twente, Enschede, The Netherlands
Interests: process-/behavior- analytics; learning analytics; feedback/recommendation automation; visual analytics dashboards; model-driven engineering; low code; business intelligence and applications; explainable AI; educational technology

Special Issue Information

Dear Colleagues,

In the current era of information abundance, the ability to distill actionable knowledge from massive datasets is critical. Interactive visualizations offer a dynamic and intuitive avenue for researchers, analysts, and decision makers to navigate complex data landscapes. By merging data-driven narratives with user-driven exploration, interactive visualizations empower users to uncover patterns, trends, and correlations that might otherwise remain obscured. Moreover, these visualizations provide a scalable solution to the challenges of rendering and comprehending extensive datasets, enabling users to zoom in on specific areas of interest and thus enhance interpretability. While interactive visualizations hold immense promise, there is a significant gap in scientific literature within the domain. Challenges such as ensuring real-time responsiveness with intuitive interface design and establishing standardized practices need further exploration. We encourage submissions that address these gaps, present innovative design methodologies, and propose novel evaluation frameworks. Our Special Issue serves as a platform to catalyze research at the intersection of big data and interactive visualizations. By fostering discussions on effective techniques, novel approaches, and emerging trends, we aim to advance the capabilities of visual analytics domain and interactive visualizations specifically in extracting actionable insights in the era of big data. Researchers and practitioners are invited to contribute their original work to enrich our collective understanding and pave the way for a more informed and data-driven future.

Submission Guidelines:

Contributions should ideally address the existing gaps in literature and advance the field, such as challenges with innovative methodologies and solutions, comparative analysis, as well as practical applications. The scope of use cases encompasses a broad spectrum of domains as diverse as education (e.g., learning analytics dashboards), financial analysis, marketing and customer analytics, sports analytics, varied recommender systems for domains of agri-food (e.g., crop management, culinary, dietary, and healthy lifestyle), supply chain management, energy consumption and sustainability, transportation and traffic management, fraud pattern detection and law enforcement, climate and environmental monitoring, urban planning, smart cities with the Internet of Things (IoT), healthcare such as medical imaging, clinical data analysis, disease tracking, neuroscience, surgical planning and simulation, and beyond.

Each submission will undergo a peer-review process to ensure the highest standards of quality and relevance, thereby fostering a collection of contributions that significantly advance the field of interactive visualizations and their transformative impact across diverse domains.

Dr. Gayane Sedrakyan
Guest Editor

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. Information is an international peer-reviewed open access monthly 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 1600 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

  • interactive visualizations
  • information visualization
  • visual analytics
  • scalable visualizations
  • big data visualizations
  • data visualizations
  • novel visualizations techniques
  • interactive data exploration
  • dynamic visualizations
  • collaborative visualizations
  • text and document visualization
  • visual analytics dashboards
  • KPI visualizations
  • interactive dashboards
  • interactive charts and graphs
  • interactive 3D visualizations
  • data driven interaction
  • responsive visualizations
  • interactive visualization tools
  • hybrid visualization approaches
  • cognitive load in interactive visualizations
  • engaging visualizations
  • interactive data presentation
  • adaptive visualization interfaces

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Published Papers (2 papers)

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Research

26 pages, 6585 KiB  
Article
Exploring Intellectual Property in the Digital Realm: A Bibliometric Study on Research on the Management and Protection of Data-Based Intellectual Property
by Hanyue Sun, Jiajia Liu, Bingyuan Chen and Le Yang
Information 2024, 15(12), 780; https://doi.org/10.3390/info15120780 - 5 Dec 2024
Viewed by 809
Abstract
The management and protection of data-based intellectual property have attracted increasing attention in the academic community due to the rapid development of digital technologies and data-driven industries. However, a comprehensive and multidimensional examination of the research landscape is still required to better understand [...] Read more.
The management and protection of data-based intellectual property have attracted increasing attention in the academic community due to the rapid development of digital technologies and data-driven industries. However, a comprehensive and multidimensional examination of the research landscape is still required to better understand its structure and evolution. Using CiteSpace software, this study conducts a bibliometric analysis, revealing key trends and patterns in collaboration, co-citation, and keyword co-occurrence in the field of data-based intellectual property. Our findings show a growing body of literature on data IP management, with a significant increase in publications since 2013. We identify that collaboration between regions, especially the United States, China, and the United Kingdom, leads global efforts, but institutional collaboration remains underdeveloped. In terms of co-citation, seminal works by Jaffe, Hall, and Samuelson form the foundation of the current research, while emerging research focuses on technological innovations like blockchain and AI. The analysis further reveals that future research is likely to explore the intersections of data privacy, innovation, and legal frameworks. Compared with previous studies, this paper builds a knowledge framework for data-based intellectual property management from a holistic perspective of bibliometrics, analyses the current challenges, and outlines future research directions, which is of significant reference value to both scholars and practitioners. Full article
(This article belongs to the Special Issue Interactive Visualizations: Design, Technologies and Applications)
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15 pages, 473 KiB  
Article
Semi-Supervised Learning for Multi-View Data Classification and Visualization
by Najmeh Ziraki, Alireza Bosaghzadeh and Fadi Dornaika
Information 2024, 15(7), 421; https://doi.org/10.3390/info15070421 - 22 Jul 2024
Cited by 1 | Viewed by 1560
Abstract
Data visualization has several advantages, such as representing vast amounts of data and visually demonstrating patterns within it. Manifold learning methods help us estimate lower-dimensional representations of data, thereby enabling more effective visualizations. In data analysis, relying on a single view can often [...] Read more.
Data visualization has several advantages, such as representing vast amounts of data and visually demonstrating patterns within it. Manifold learning methods help us estimate lower-dimensional representations of data, thereby enabling more effective visualizations. In data analysis, relying on a single view can often lead to misleading conclusions due to its limited perspective. Hence, leveraging multiple views simultaneously and interactively can mitigate this risk and enhance performance by exploiting diverse information sources. Additionally, incorporating different views concurrently during the graph construction process using interactive visualization approach has improved overall performance. In this paper, we introduce a novel algorithm for joint consistent graph construction and label estimation. Our method simultaneously constructs a unified graph and predicts the labels of unlabeled samples. Furthermore, the proposed approach estimates a projection matrix that enables the prediction of labels for unseen samples. Moreover, it incorporates the information in the label space to further enhance the accuracy. In addition, it merges the information in different views along with the labels to construct a consensus graph. Experimental results conducted on various image databases demonstrate the superiority of our fusion approach compared to using a single view or other fusion algorithms. This highlights the effectiveness of leveraging multiple views and simultaneously constructing a unified graph for improved performance in data classification and visualization tasks in semi-supervised contexts. Full article
(This article belongs to the Special Issue Interactive Visualizations: Design, Technologies and Applications)
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