Article
Computer Science, Software Engineering
Jose Matute, Lars Linsen
Summary: The use of parallel axes in visualizing multidimensional data is popular, but there is a lack of clear strategies for representing categories and discretizing continuous ranges. Traditional and new approaches were evaluated, and the hybrid approach was found to provide more accurate results and faster response times for probability-based queries.
COMPUTER GRAPHICS FORUM
(2022)
Article
Computer Science, Software Engineering
Conny Walchshofer, Andreas Hinterreiter, Kai Xu, Holger Stitz, Marc Streit
Summary: This article proposes a novel visual approach to the meta-analysis of interaction provenance, which helps to understand user behavior patterns and visual analysis strategies. By capturing user sessions as graph representations and using different types of two-dimensional embeddings, patterns for data types and analytical reasoning strategies can be extracted.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Computer Science, Software Engineering
Dora Kiesel, Patrick Riehmann, Bernd Froehlich
Summary: This paper introduces new techniques for seamless transitions between parallel coordinate plots, star plots, and scatter plots. The star plot serves as a mediator visualization between parallel coordinate plots and scatter plots, and a variant of the star plot called polycurve star plot is developed to improve space utilization and cluster detection. A geometrically motivated method is also proposed to embed scatter points into star plots and parallel coordinate plots to track the transition of structural information. The integration of these techniques into an interactive analysis tool demonstrates their advantages over a multi-view approach.
COMPUTER GRAPHICS FORUM
(2023)
Article
Computer Science, Software Engineering
Alessio Arleo, Christos Tsigkanos, Roger A. Leite, Schahram Dustdar, Silvia Miksch, Johannes Sorger
Summary: This paper presents Sabrina 2.0, a Visual Analytics approach for exploring financial data across different scales. It integrates heterogeneous information sources and generates firm-to-firm financial transaction networks. The evaluation with domain experts demonstrates its ability to generate insights and assist users in exploring a national economy.
COMPUTER GRAPHICS FORUM
(2023)
Article
Computer Science, Software Engineering
N. Brich, C. Schulz, J. Peter, W. Klingert, M. Schenk, D. Weiskopf, M. Krone
Summary: We propose an approach for analyzing high-dimensional measurement data in intensive care units, which have varying sampling rates. Our approach combines projection-based time curves and small multiples to reduce data complexity and supports analysis of both individual patients and ensembles. We received positive feedback from domain scientists in the surgical department through evaluation with real-world data.
COMPUTER GRAPHICS FORUM
(2022)
Article
Computer Science, Software Engineering
E. Zohrevandi, C. A. L. Westin, J. Lundberg, A. Ynnerman
Summary: Operators in air traffic control require efficient real-time processing of complex data for safety-critical situations. Automation support tools aid controllers in preventing separation losses between aircraft. This study introduces two visual analytics interfaces aimed at improving information integration and supporting controllers in prioritizing conflict resolution by promoting contextual awareness and 'what-if' and 'what-else' probes.
COMPUTER GRAPHICS FORUM
(2022)
Article
Computer Science, Software Engineering
Q. Wang, R. S. Laramee
Summary: With the rapid growth of archived digital health records, advanced visualization and visual analytics systems are required to uncover valuable insight. Interactive EHR and PopHR visualization systems have been proposed to support effective clinical analysis and decision making. The field presents trends and challenges, calling for further research and data classification.
COMPUTER GRAPHICS FORUM
(2022)
Article
Computer Science, Software Engineering
Matthias Miller, Daniel Fuerst, Hanna Hauptmann, Daniel A. Keim, Mennatallah El-Assady
Summary: This article proposes an approach to enhance the traditional music analysis workflow by complementing it with interactive visualization entities. Through gradual transitions and design-driven visual query filters, analysts can retrace and comprehend the relationship between common music notation (CMN) and abstract data representations, investigating statistical and semantic patterns.
COMPUTER GRAPHICS FORUM
(2022)
Article
Computer Science, Software Engineering
Velitchko Filipov, Alessio Arleo, Silvia Miksch
Summary: Networks are abstract and ubiquitous data structures consisting of data points and their relationships. Network visualization helps researchers understand connections, gain insights, and detect patterns. In this paper, we provide a meta-survey that discusses and categorizes recent surveys and task taxonomies in the field of network visualization. We also analyze the varying support of available task taxonomies and establish a classification. This research provides an overview and roadmap for current trends and future work in network visualization.
COMPUTER GRAPHICS FORUM
(2023)
Article
Computer Science, Information Systems
Bo Yang, Dong Tian, Guihua Shan
Summary: This study presents a multi-module visualization framework and implements a visual analysis system called TobaccoGeoVis for efficient analysis of tobacco spatial data. The system provides a visualization technology for overlaying multiple graphics on a map and adopts artificial intelligence algorithms and multiview linkage interactive methods, enabling flexible data-attribute field mapping and graphical parameter configuration.
Article
Chemistry, Multidisciplinary
Sofia Karam, Raed Jaradat, Michael A. Hamilton, Vidanelage L. Dayarathna, Parker Jones, Randy K. Buchanan
Summary: In the field of visual analytics, introducing appropriate natural interaction techniques is necessary for building effective and enjoyable systems. This research introduces a novel virtual reality module to explore new interaction techniques and evaluates its effectiveness compared to traditional desktop settings.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Guodao Sun, Zihao Zhu, Gefei Zhang, Chaoqing Xu, Yunchao Wang, Sujia Zhu, Baofeng Chang, Ronghua Liang
Summary: Mathematical optimization is used to find the best parameters in a search space and has been widely applied in computer science, engineering, operations research, and economics. It has also been extended to data visualization to improve data processing and exploration. However, there is a lack of comprehensive summarization of mathematical optimization in data visualization.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Software Engineering
Zhuochen Jin, Shunan Guo, Nan Chen, Daniel Weiskopf, David Gotz, Nan Cao
Summary: This paper introduces a visual analytics method for recovering causalities in event sequence data by extending the Granger causality analysis algorithm to Hawkes processes and incorporating user feedback for causal model refinement. The visualization system supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. Evaluation includes quantitative improvements in model from user feedback mechanism and qualitative usefulness demonstrated through case studies in different domains.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2021)
Article
Computer Science, Software Engineering
Yi Guo, Shunan Guo, Zhuochen Jin, Smiti Kaul, David Gotz, Nan Cao
Summary: This paper reviews the state-of-the-art visual analytics approaches for event sequence data and categorizes them based on analytical tasks and applications. The authors also identify several remaining research challenges and future research opportunities.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Software Engineering
Natalia Andrienko, Gennady Andrienko, Gota Shirato
Summary: This study investigates the problem of analysing episode-based data and develops a general approach to address this issue. The approach transforms the set of episodes into text documents and uses topic modelling techniques to identify co-occurring words that represent patterns of variation in multiple attributes. Analysts can interpret the topics and examine their distribution across all episodes through interactive visualizations.
COMPUTER GRAPHICS FORUM
(2023)