PUMA publications for /tag/VisualizationFri Apr 17 09:49:16 CEST 2026IECON 2025 -- 51st Annual Conference of the IEEE Industrial Electronics Society1--6Analysis and Fault-Tolerant Control of Permanent-Magnet Synchronous Machines under Multiple Open-Switch Converter Faults2025Current_control Fault_diagnosis Fault_tolerance Fault_tolerant_systems Standards Synchronous_Machines Torque_measurement Total_harmonic_distortion Visualization Voltage_control Voltage_source_inverters hp_iew open-switch_faults permanent-magnet_synchronous_machines reliability Open-switch faults in voltage source inverters significantly affect the operation of permanent-magnet synchronous machines, especially when multiple switch faults occur simultaneously. This paper presents a comprehensive analysis of the remaining feasible voltages under different combinations of double open-switch faults. Based on this analysis, a fault-tolerant control strategy is discussed that aims to minimize torque ripple and prevent overcurrents. The presented visualization of remaining feasible voltages along with the approach for fault-tolerant control allow for a classification of fault combinations according to their impact on control performance and machine behavior, including the identification of a worst-case combination of double open-switch faults. Experimental results obtained on a laboratory test bench validate the effectiveness of the proposed control strategy and confirm the identified worst-case fault combination.Wed Sep 24 15:19:38 CEST 20252025 IEEE Visualization and Visual Analytics (VIS)AnnoLens: Exploration and Annotation through Lens-Based Guidance2025myown visualization visus:beckerfa visus:blaschta visus:kochsn Thu Jun 12 11:45:12 CEST 2025EuroVis Workshop on Visualization Play, Games, and ActivitiesPlaying with Knowledge: Leveraging Visualization Games for Data Validation and Inspiration2025myown visualization visus:beckerfa visus:blaschta We present an approach to use visualization games for data validation and inspiration in a collaborative coding context. As part of an interactive coding system that lets coders create a tag hierarchy and tag data items, we designed multiple games that support validating that data and exploring it in a novel way. Each game has mechanics inspired by existing games and incorporates visualization and externalization to varying degrees. By playing these games, coders randomly sample the data space to pinpoint problems and find inspiration, like discovering gaps in the data or contemplating novel item-tag combinations. Game results are automatically tracked to let coders analyze their performance and find out in which cases they tend to make mistakes. Coders can also create objection notes at the end of a game to externalize insights which are accessible in other parts of the system. For example, if a coder is convinced that an item should not have a specific tag they were shown in a game, they can create an objection about this issue that all system users can see. Our games can be played with different datasets at https://arielmant0.github.io/collacode/?tab=games.Thu Jun 12 11:42:44 CEST 2025Seamless Collaborative Coding with Visualization2025myown visualization visus:beckerfa visus:blaschta Tue Jun 10 14:20:28 CEST 2025Computer Graphics ForummayBeyond Entertainment: An Investigation of Externalization Design in Video Games2025myown visualization visus:beckerfa visus:blaschta Fri Dec 06 15:08:23 CET 2024ChamSemantic Systems. In the Era of Knowledge Graphs70--86QueDI: From Knowledge Graph Querying to Data Visualization2020knowledgegraph visualization While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on experts in table manipulation, such as OD experts) in querying and exploiting LOD by taking advantage of our target users' expertise in table manipulation and chart creation.Fri Dec 06 15:05:54 CET 2024BMC Medical Informatics and Decision Makingjun2147Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology222022knowledgegraph visualization 02Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.Fri Dec 06 14:30:01 CET 2024IEEE Transactions on Visualization and Computer Graphicsjan1584-594Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities302024knowledgegraph visualization This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners – KG Builders, Analysts, and Consumers – each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.Fri Dec 06 14:18:30 CET 20242024 IEEE 17th Pacific Visualization Conference (PacificVis)April162-171KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations2024knowledgegraph visualization IEEE Transactions on Visualization and Computer Graphics (TVCG) publishes cutting-edge research in the fields of visualization, computer graphics, and virtual and augmented realities. Within the TVCG ecosystem, different stakeholders make decisions based on available information related to TVCG almost on a daily basis. The decisions involve various tasks such as the retrieval of research ideas and trends, the invitation of peer reviewers, and the selection of editorial board members, just to name a few. To make well-informed decisions in these contexts, a data-driven approach is necessary. However, the current IEEE digital library only provides access to individual papers. Transforming this wealth of data into valuable insights is a daunting task, requiring specialized expertise and effort in tasks such as data crawling, cleaning, analysis, and visualizations. To address the needs of the community in facilitating more efficient and transparent decision-making, we construct and publicly release a TVCG knowledge graph (TVCG-KG). TVCG-KG is a structured representation of heterogeneous information, including the metadata of each publication such as author, affiliation, title, and semantic information such as method, task, data. Despite the widespread use of KGs in various downstream applications, a noticeable gap exists in the visualization literature regarding the full exploitation of the rich semantics embedded within KGs. While it might seem intuitive to just employ interactive graph-based visualization for KGs, we propose that knowledge discovery over KG is a series of visual exploratory tasks that can benefit from using multiple visualization techniques and designs. We conducted an evaluation of TVCG-KG quality and demonstrated its practical utility through several real-world cases. Our data and code are accessible via the following URL: https://github.com/yasmineTYM/TVCG-KG.git.Fri Dec 06 14:14:25 CET 2024KG Explorer: a Customisable Exploration Tool for Knowledge Graphsproceedings2021knowledgegraph visualization The growing adoption of Knowledge Graphs demands new applications which enable users to search and browse structured data in a suitable way depending on the domain. In this paper, we introduce KG Explorer, a web-based exploratory search engine for RDF-based Knowledge Graphs. The software can be configured in order to adapt to different information domains, customising both the UI components and the queries made for retrieving the information. It also includes features such as full-text search, facet-based advanced search, and the possibility to create lists of favourites items modelled in the knowledge graph.Fri Dec 06 14:11:50 CET 2024New York, NY, USAProceedings of the ACM first workshop on CyberInfrastructure: information management in eScience1139–46CIMS '07RDF data exploration and visualization2007knowledgegraph visualization 9We present Paged Graph Visualization (PGV), a new semi-autonomous tool for RDF data exploration and visualization. PGV consists of two main components: a) the "PGV explorer" and b) the "RDF pager" module utilizing BRAHMS, our high per-formance main-memory RDF storage system. Unlike existing graph visualization techniques which attempt to display the entire graph and then filter out irrelevant data, PGV begins with a small graph and provides the tools to incrementally explore and visualize relevant data of very large RDF ontologies. We implemented several techniques to visualize and explore hot spots in the graph, i.e. nodes with large numbers of immediate neighbors. In response to the user-controlled, semantics-driven direction of the exploration, the PGV explorer obtains the necessary sub-graphs from the RDF pager and enables their incremental visualization leaving the previously laid out sub-graphs intact. We outline the problem of visualizing large RDF data sets, discuss our interface and its implementation, and through a controlled experiment we show the benefits of PGV.Fri Dec 06 14:07:49 CET 2024IEEE Transactions on Visualization and Computer Graphicsdec127702-7716KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance302024knowledgegraph visualization Knowledge graphs have been commonly used to represent relationships between entities and are utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they did not consider various user needs and characteristics of knowledge graphs. Exploratory approaches specifically designed to uncover and summarize insights in knowledge graphs have not been well studied yet. In this article, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting the exploration of knowledge graphs with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and helping explore the entire network.Fri Dec 06 13:34:57 CET 20242020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)dec174-178Knowledge Graph Visualization: Challenges, Framework, and Implementation2020knowledgegraph tools visualization A knowledge graph (KG) is a rich resource representing real-world facts. Visualizing a knowledge graph helps humans gain a deep understanding of the facts, leading to new insights and concepts. However, the massive and complex nature of knowledge graphs has brought many longstanding challenges, especially to attract non-expert users. This paper discusses these challenges; we turned them into a generic knowledge-graph visualization framework, namely KGViz, consisting of four dimensions: modularity, intuitive user interface, performance, and access control. Our implementation of KGViz is a high-capacity, extendable, and scalable KG visualizer, which we designed to promotes community contributions.Thu Jul 11 10:05:52 CEST 2024IEEE Pervasive Computing380-83Cognition-Aware Computing132014Context hcics vis Context-aware Pervasive Sensors, Visualization bioinformatics, cognition, cognition-aware computing, electroencephalography, intelligent modeling, systems, tracking, Despite significant advances in context sensing and inference since its inception in the late 1990s, context-aware computing still doesn't implement a holistic view of all covert aspects of the user state. Here, the authors introduce the concept of cognitive context as an extension to the current notion of context with a cognitive dimension. They argue that visual behavior and brain activity are two promising sensing modalities for assessing the cognitive context and thus the development of cognition-aware computing systems.Tue Jul 09 10:42:05 CEST 2024Journal of the American Medical Informatics AssociationDesigning interactive visualizations for analyzing chronic lung diseases in a user-centered approach2024healthcare myown visualization visus:beckerfa Thu Apr 04 11:02:00 CEST 2024PiscatawayNOMS 2022 : 2022 IEEE/IFIP Network Operations and Management SymposiumVITALflow : Visual Interactive Traffic Analysis with NetFlow2022cybersecurity visualization visus:beckerfa visus:kochsn visus:mueller Mon Mar 18 16:37:00 CET 20241st Japan Visualization Symposium (JapanVis 2024)AlertSets: Supporting Exploratory Analysis of Cybersecurity Alerts through Sets2024DesignStudy VisualAnalytics cybersecurity sets visualization visus:beckerfa visus:ertl visus:mueller Security providers typically deal with large numbers of alerts based on heterogeneous data from many endpoint sensors. While the number of alerts is generally much smaller than the volume of raw data, most alerts are false positives that do not reflect genuinely malicious activity. All types of experts work on such alerts, be it to determine whether they are indeed false positives, to build machine learning models to support their analysis or to keep an eye on the current threat landscape. We conducted a design study to support a diverse group of experts whose working environments are connected to the same alert data. Based on an ongoing industry project that clusters vectorized alerts, we designed and evaluated a visual analytics system enabling exploration via powerful, easy-to-understand filtering mechanisms framed through set operations. In this article, we describe our system, give a detailed breakdown of the design process and the lessons we learned. Lastly, we discuss the results from expert interviews, which showed the set-based framing to align with experts’ intuitive approach to data analysis and helped users uncover improvement opportunities for the clustering pipeline.Mon Mar 18 16:22:18 CET 2024IEEE Symposium on Visualization for Cyber Security (VizSec)Interactive Process Tree Analysis: Exploring the Behaviour of Processes with Visual Analytics for Security Operators2021cybersecurity myown visualization visus:beckerfa visus:ertl visus:mueller visus:rapprt Despite constant efforts to improve automation for IT security incidents, analysts are often confronted with numerous alerts and have to make sure that they do not miss the most critical of them. The analysts need to quickly decide based on a plethora of yet incomplete information. This information often includes a tree of parent and child processes in real-world scenarios. We present an augmented visualisation of such a process tree, which not only shows the static hierarchy as previous ones do, but also conveys the temporal relation between processes, thus allowing for investigating the hierarchy and time perspective of the process tree at the same time. Furthermore, it makes additional process-related events collected by endpoint sensors accessible for a more complete view on process behaviour.Mon Mar 18 16:15:47 CET 20242024 IEEE 17th Pacific Visualization Symposium (PacificVis)TimeSeriesMaker: Interactive Time Series Composition in No Time2024TimeSeries visualization visus:beckerfa visus:blaschta TimeSeriesMaker is an open-source application to visually compose time series data in an intuitive and shareable manner. Visualization researchers often use time series data in studies about perceptual or cognitive phenomena and many other contexts. However, finding or generating time series data that fits a given scenario is not always easy. Using a component-based architecture, TimeSeriesMaker allows analysts to compose time series data with complex patterns by combining different components, such as noise, a linear trend or a seasonal pattern. An interactive compositor tree of these components lets analysts explore their combinations using different operators. We support reproducibility and transparency by including functionalities that allow analysts to export and share their configuration, which others can use to reload and modify the same time series. In a qualitative online study with visualization researchers, we found that our approach enables them to create a time series based on an example image or their own requirements. However, system usability could be further improved when interacting with the compositor tree. TimeSeriesMaker can be found here: https://unistuttgart-visus.github.io/time-series-maker/.Mon Mar 18 11:22:16 CET 2024EuroVis 2023 - PostersPutting Annotations to the Test2023DigitalPen annotations evaluation myown visualization visus:beckerfa visus:ertl VisualizationCommunity for tag(s) Visualization