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Applied Sciences
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Research on Information Management and Information Visualization
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Research on Information Management and Information 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: 20 July 2024 | Viewed by 2967

Special Issue Editors

Prof. Dr. Daniel Gonçalves

E-Mail Website
Guest Editor
Instituto de Engenharia de Sistemas e Computadores Investigacao e Desenvolvimento em Lisboa, Instituto Superior Técnico, University of Lisbon, 1000-029 Lisbon, Portugal
Interests: information management; information visualization; human–computer interaction
Prof. Dr. Sandra Gama

E-Mail Website
Guest Editor
Department at Instituto Superior Técnico (IST), Universidade de Lisboa, 1649-004 Lisbon, Portugal
Interests: HCI (human–computer interaction); human factors in HCI; information visualization; gamification; BCI (brain–computer interfaces)

Special Issue Information

Dear Colleagues,

In today’s world, it is harder than ever to understand the plethora of information continuously at our fingertips. In fact, we can now generate large amounts of data using a multitude of devices and applications, such as smart meters, sensors, online services, and explicit data-gathering initiatives that pervade our daily lives. Alas, while generating such data is almost trivial, extracting actionable information from these data is anything but. Not only is the sheer amount of data a hurdle, but their heterogeneity also leads to the problem of fragmentation—our information is scattered and difficult to comprehend in a cohesive manner. While browsing and navigation are difficult, pattern finding is virtually impossible.

Additionally, the advent of mobile and wearable devices gave rise to the creation, implicitly or explicitly, of large amounts of personally relevant information. While this raises privacy issues, it also opens up new and interesting avenues of insight into ourselves, our lives and activities. Still, the problem of achieving a cohesive, holistic understanding of this information remains, compounded by the need to include personal semantics in the mix.

A way to make sense of all this information is through the use of interactive systems in general and information visualization in particular. Through this, we can not only obtain a general view of our data, but can also navigate, filter and manipulate these data in rich and meaningful ways. While making it possible to verify context knowledge and see patterns in our data, information visualization alleviates the cognitive load associated with data interpretation.

Prof. Dr. Daniel Gonçalves
Prof. Dr. Sandra Gama
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

  • big data visualization
  • novel information communication techniques
  • human factors in information system visualization
  • information management design studies
  • information management and visualization systems
  • interactive systems and applications and techniques for information management
  • managing information in a mobile context
  • personal information management
  • personal information visualization
  • using AR and VR to manage information

Published Papers (3 papers)

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Research

25 pages, 18140 KiB  
Article
Online Dynamic Network Visualization Based on SIPA Layout Algorithm
by Guijuan Wang, Huarong Chen, Rui Zhou, Yadong Wu, Wei Gao, Jing Liao and Fupan Wang
Appl. Sci. 2023, 13(23), 12873; https://doi.org/10.3390/app132312873 - 30 Nov 2023
Viewed by 585
Abstract
Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based [...] Read more.
Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods. Full article
(This article belongs to the Special Issue Research on Information Management and Information Visualization)
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18 pages, 2041 KiB  
Article
Accepting the Digital Challenge: Public Perceptions and Attitudes toward Interactivity in Data Journalism
by Boning Zhang, Yuxin Zhang and Younghwan Pan
Appl. Sci. 2023, 13(19), 10857; https://doi.org/10.3390/app131910857 - 29 Sep 2023
Viewed by 901
Abstract
In the context of digitization, traditional journalism is facing transformation and innovation. Among them, the interactivity of visual graphics in data journalism is crucial for attracting and retaining online users, but few studies have examined public perceptions and attitudes toward it. In this [...] Read more.
In the context of digitization, traditional journalism is facing transformation and innovation. Among them, the interactivity of visual graphics in data journalism is crucial for attracting and retaining online users, but few studies have examined public perceptions and attitudes toward it. In this study, we proposed a model to validate the relationship between users’ perceived interactivity and their attitudes toward data journalism, and we included user affective and cognitive factors (enjoyment and engagement) related to this as possible mediating variables in the model for validation. We conducted experiments (n = 75) using data journalism containing map visualizations with three levels of interactivity (low, medium, and high) in China. Furthermore, an exploratory evaluation of the experimental group provided further insights into the differences in interactions between groups, and the emerging five key concepts of data journalism design. Overall, all our hypotheses are supported, with enjoyment and engagement mediating the relationship between perceived interactivity and users’ attitudes toward the news. In addition, the experimental group with higher interaction potential also reported more positive attitudes toward journalism. Therefore, if data journalism and visualization designers want to attract and retain users in the future, enhancing user interaction on news pages will be a proven method. Full article
(This article belongs to the Special Issue Research on Information Management and Information Visualization)
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25 pages, 19170 KiB  
Article
Investigating the Impact of Different Partial Overlap Levels on the Perception of Visual Variables for Categorical Data
by Diego Santos, Alexandre Freitas, Rodrigo Lima, Carlos Gustavo Santos and Bianchi Meiguins
Appl. Sci. 2023, 13(16), 9268; https://doi.org/10.3390/app13169268 - 15 Aug 2023
Viewed by 1030
Abstract
The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. [...] Read more.
The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. This paper aims to present a comparative study of the reading of visual variables values with partial overlap. The study focuses on categorical data representations varying the percentage limits of partial overlap and the number of distinct values for each visual variable: hue, lightness, saturation, shape, text, orientation, and texture. A computational application generated random scenarios for a unique visual pattern target to perform location tasks. Each scenario involved presentation of the visual items in a grid layout with 160 elements (10 × 16), each visual variable had from three to five distinct values encoded, and the partial overlap percentages applied, represented by a gray square in the center of each grid element, were 0% (control), 50%, 60%, and 70%. Similar to the preliminary tests, the tests conducted in this study involved 48 participants organized into four groups, with 126 tasks per participant, and the application captured the response and time for each task performed. The results analysis indicated that the hue, lightness, and shape visual variables were robust to high percentages of occlusion and gradual increase in encoded visual values. The text visual variable showed promising results for accuracy, and the resolution time was a little higher than for the last visual variables mentioned. In contrast, the texture visual variable presented lower accuracy to high levels of occlusion and more different visual encoding values. Finally, the orientation and saturation visual variables exhibited the highest error and worst perfomance rates during the tests. Full article
(This article belongs to the Special Issue Research on Information Management and Information Visualization)
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