Interactive Visualization System Research Articles

Interactive Web-Based Visual Analysis on Network Traffic Data

Network traffic data analysis is important for securing our computing environment and data. However, analyzing network traffic data requires tremendous effort because of the complexity of continuously changing network traffic patterns. To assist the user in better understanding and analyzing the network traffic data, an interactive web-based visualization system is designed using multiple coordinated views, supporting a rich set of user interactions. For advancing the capability of analyzing network traffic data, feature extraction is considered along with uncertainty quantification to help the user make precise analyses. The system allows the user to perform a continuous visual analysis by requesting incrementally new subsets of data with updated visual representation. Case studies have been performed to determine the effectiveness of the system. The results from the case studies support that the system is well designed to understand network traffic data by identifying abnormal network traffic patterns.

Visualization and Data Analysis of Multi-Factors for the Scientific Research Training of Graduate Students

With the change of graduate education from quantity expansion to quality promotion, how to improve the quality of graduate cultivation has aroused wide concern. However, existing scientific quantitative methods tend to investigate the results of graduate training, with a lack of attention to the multidimensional data during the training process. Thus, exploratory analysis of multidimensional data in the graduate training process and accurate grasp of the key process factors affecting graduate academic competence is an indispensable task for achieving the stated goals of graduate education. In this paper, a visual analytic system of graduate training data is proposed to help users implement in-depth analysis based on the graduate training process. First, a questionnaire is designed about the training process to identify multidimensional data timely and accurately. Then, a series of data mining methods are utilized to further detect key factors in the training process, which will be used to make academic predictions for first-year graduates. Meanwhile, an interactive visual analytic system has been developed to help users understand and analyze the key factors affecting the graduate training process. Based on the results of the visual analysis, effective suggestions will be provided for graduate students, supervisors, and university administrators to improve the quality of graduate education.

Seeing is believing: Towards interactive visual exploration of data privacy in federated learning

Federated learning (FL), as a popular distributed machine learning paradigm, has driven the integration of knowledge in ubiquitous data owners under one roof. Although designed for privacy-preservation by nature, the supposed well-sanitized parameters still convey sensitive information (e.g., reconstruction attack), while existing technical countermeasures provide weak explainability for privacy understanding and protection practices of general users. This work investigates these privacy concerns with an exploratory study and elaborates on data owners’ expectations in FL. Based on the analysis, we design the first interactive visualization system for FL privacy that supports intelligible privacy inspection and adjustment for data owners. Specifically, our proposal facilitates sample recommendation for joint privacy–performance training at cold start. Then it provides visual interpretation and attention rendering of privacy risks in view of multiple attacking channels and a holistic view. Further it supports interactive privacy enhancement involving both user initiative and differential privacy technique, and iterative trade-off with real-time inference accuracy estimation. We evaluate the effectiveness of the system and collect qualitative feedbacks from users. The results demonstrate that 96.7% of users acknowledge the benefits to privacy inspection and adjustment and 90.3% are willing to use our system. More importantly, 87.1% increase the willingness of contributing data for FL.

Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.

DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification.

In many applications, developed deep-learning models need to be iteratively debugged and refined to improve the model efficiency over time. Debugging some models, such as temporal multilabel classification (TMLC) where each data point can simultaneously belong to multiple classes, can be especially more challenging due to the complexity of the analysis and instances that need to be reviewed. In this article, focusing on video activity recognition as an application of TMLC, we propose DETOXER, an interactive visual debugging system to support finding different error types and scopes through providing multiscope explanations.

MedCV: An Interactive Visualization System for Patient Cohort Identification from Medical Claim Data.

Healthcare providers generate a medical claim after every patient visit. A medical claim consists of a list of medical codes describing the diagnosis and any treatment provided during the visit. Medical claims have been popular in medical research as a data source for retrospective cohort studies. This paper introduces a medical claim visualization system (MedCV) that supports cohort selection from medical claim data. MedCV was developed as part of a design study in collaboration with clinical researchers and statisticians. It helps a researcher to define inclusion rules for cohort selection by revealing relationships between medical codes and visualizing medical claims and patient timelines. Evaluation of our system through a user study indicates that MedCV enables domain experts to define high-quality inclusion rules in a time-efficient manner.

On Sense Making and the Generation of Knowledge in Visual Analytics

Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perception and cognition, in which human analysts perceive external representations (system structure, dataset, integral data visualizations) and form respective internal representations (internal cognitive imprints of external systems) that enable deeper comprehension of the employed system and the underlying data features. These internal representations further evolve through continuous interaction with external representations. They also depend on the individual’s own cognitive pathways. Currently, there has been insufficient work on understanding how these internal cognitive mechanisms form and function. Hence, we aim to offer our own interpretations of such processes observed through our daily data exploration workflows. This is accomplished by following specific explorative data science tasks while working with diverse interactive visual systems and related notebook style environments that have different organizational structures and thus may entail different approaches to thinking and shaping sense making and knowledge generation. In this paper, we deliberate on the cognitive implications for human analysists when interacting with such a diverse organizational structure of tools and approaches when performing the essential steps of an explorative visual analysis.

NPIPVis: A visualization system involving NBA visual analysis and integrated learning model prediction

BackgroundData-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. MethodsThis study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology, and iStoryline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a teamʹs wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and RandomUnderSampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using Grid-SearchCV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive exPlanations (SHAP) model was introduced to enhance the interpretability of the model. ResultsThe experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. ConclusionsThis study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players; this can also be extended to other sports events or related fields.

Interactive Knowledge Visualization Based on IoT and Augmented Reality

In order to solve the integration value of information technology and education, it is mainly reflected in the container for storing and disseminating information, the problem is that learners lack the proper self-learning ability, and the author proposes an interactive knowledge visualization system based on the Internet of Things and augmented reality technology. According to the applicable characteristics of augmented reality technology applied to IoT data presentation and interaction, the method can analyze and describe its application possibility. In the design of interactive electronic technology computer-aided teaching system based on NET platform, the system hardware structure consists of user interface layer, business selection layer, and data management layer. Users such as teachers and students enter their identity information at the user interface layer to log in to the system and enter the business selection layer and click the corresponding program according to their application requirements, the service selection layer transmits the user’s selection instruction to the data management layer, and the data management layer selects the corresponding resources according to the user’s needs and feeds it back to the user. The interaction of the system is mainly reflected in interactive teaching and information interaction, and interactive teaching is reflected in the online teaching of teachers and students. Information interaction is embodied in the information transmission of the system information interaction model. Experimental results show that after applying the system, the number of students with high self-efficacy increased from 11 to 21 and the proportion increased from 21.4% to 34.8%. The system designed by the author has strong antipressure ability, can respond to the application instructions of a large number of users in real time, and has a good interactive teaching effect, which improves the self-efficacy of students.

TriPlan: an interactive visual analytics approach for better tourism route planning.

Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets.Graphical abstract Electronic supplementary materialThe online version of this article (10.1007/s12650-022-00861-8) contains supplementary material, which is available to authorized users.

A uncertainty visual analytics approach for bus travel time

Bus travel time is uncertain due to the dynamic change in the environment. Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks. To quantify the uncertainty of the bus travel time prediction model, a visual analysis method about the bus travel time uncertainty is proposed in this paper, which can intuitively obtain uncertain information of bus travel time through visual graphs. Firstly, a Bayesian encoder–decoder deep neural network (BEDDNN) model is proposed to predict the bus travel time. The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty. Second, an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines. The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties. Finally, the model evaluation results based on actual bus data illustrate the effectiveness of the model. The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.

MDIVis: Visual analytics of multiple destination images on tourism user generated content

Abundant tourism user-generated content (UGC) contains a wealth of cognitive and emotional information, providing valuable data for building destination images that depict tourists’ experiences and appraisal of the destinations during the tours. In particular, multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations. However, existing methods usually focus on the image of a single destination, and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge. Therefore, we discuss requirements with tourism experts and present MDIVis, a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison. Specifically, we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images. Finally, we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data, and the usability and effectiveness are confirmed through expert interviews.

Advanced tools for guiding data‐led research processes of Upper‐Atmospheric phenomena

AbstractThis paper presents tools that help researchers implement the processes of data‐led studies of upper‐atmospheric phenomena. These tools were developed as a part of the activities of the Inter‐university Upper atmosphere Global Observation NETwork (IUGONET) of Japan, which is a project to develop infrastructure for upper‐atmospheric research data. This paper focuses on the data service named IUGONET Type‐A, which was launched in October 2016 and has since evolved. In addition to being a conventional metadata catalogue, it has many other useful functions: an easy cross‐searching system, a quick‐look data‐plotting procedure, an interactive data visualization system named UDAS web, and strong linkage with analysis software. Users can pick up relevant data from a huge number of data sets using either lists categorized by instruments/projects, observed regions and special campaigns or a world map of observatories. Users can quickly find the time, location and nature of phenomena that occurred by comparing the quick‐look plots of various data displayed by the browser. UDAS web allows researchers to interactively create stacked plots of various data types that can facilitate the understanding of the relationships among phenomena observed in different regions. Furthermore, it presents a command list for software dedicated to data analysis that can smoothly lead users to perform detailed analyses. IUGONET Type‐A provides a one‐stop data service that can assist users in searching, examining and comprehending data for advanced analysis. It is also capable of handling old data, including analogue data and written paper documents. Thus, it will provide useful support for innovative interdisciplinary scientific research on solar–terrestrial phenomena.

Innovation of Tin Oxide Ceramic Manufacturing Process Based on WSN and Remote Visualization Technology.

A wireless sensor network (WSN) collects information from small sensors, processes the information effectively through a built-in computing system, forms a dynamic network topology in a self-organizing method, and sends it to the base station through wireless communication. At the same time, the scale of data generated by high-performance scientific computing has rapidly increased from GB and TB to EB. The traditional scientific vision postprocessing model that initially saved the data and then performed the vision processing became a very time-consuming and troublesome process due to the limitation of disk I/O efficiency, which seriously affected the development of technology, and the accuracy was relatively low. The development of high-performance computer parallel processing technology has promoted parallel visualization, and the expansion of Internet bandwidth has accelerated remote visualization. SnO2, or tin oxide, is a kind of N-type semiconductor material with a rutile structure. The crystal structure is stable, the corrosion resistance is good, the melting point is high, the resistance after doping is low, and the sinterability is good. Materials that are sensitive to optics, electrodes, and gases have a wide range of uses. This paper proposes a new data fusion algorithm based on BP neural network and studies the key technologies for the design and implementation of a web-based multiuser remote interactive vision system. In this paper, the effects of ZnO, MnO2, Nb2O5, CuO, and Li2CO3 doping on the conductivity and bulk density of SnO2 semiconductor ceramics were studied by means of ceramic preparation method, using intelligent resistance meter, bulk density tester, X-ray diffractometer, and scanning electron microscope, thus promoting the innovation of tin oxide ceramic preparation process.

Reusing Interactive Analysis Workflows

AbstractInteractive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code‐based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.

AirLens: Multi‐Level Visual Exploration of Air Quality Evolution in Urban Agglomerations

AbstractThe precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi‐run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi‐level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos

AbstractDanmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

AbstractWe describe LineageD—a hybrid web‐based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision‐making process, however, is time‐consuming, tedious, and error‐prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom‐up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top‐down fashion for a few steps, improving the ML‐based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top‐down building approach can reduce assignments errors in real practice.

DLA-VPS: Deep-Learning-Assisted Visual Parameter Space Analysis of Cosmological Simulations.

Cosmologists often build a mathematics simulation model to study the observed universe. However, running a high-fidelity simulation is time consuming and thus can inconvenience the analysis. This is especially so when the analysis involves trying out a large number of simulation input parameter configurations. Therefore, selecting an input parameter configuration that can meet the needs of an analysis task has become an important part of the analysis process. In this work, we propose an interactive visual system that efficiently helps users understand the parameter space related to their cosmological data. Our system utilizes a GAN-based surrogate model to reconstruct the simulation outputs without running the expensive simulation. We also extract information learned by the deep neural-network-based surrogate models to facilitate the parameter space exploration. We demonstrate the effectiveness of our system via multiple case studies. These case study results demonstrate valuable simulation input parameter configuration and subregion analyses.

DETERMINATION OF FORECAST INDICATORS OF ELECTRICITY QUALITY IN MODE OF SYNCHRONIZED VECTOR MEASUREMENTS

The work is devoted to the development of software for forecasting the quality of electricity in an automated system for diagnosing the quality of electricity consumers using cloud technologies. The existing domestic and foreign methods for monitoring the quality of electricity using the technology of synchronized vector measurements are analyzed. The structural scheme of the technology of diagnostics of electricity quality as a new direction at the junction of sciences – information technologies and energy is developed. Based on the experimental data of electricity quality indicators obtained from the synchrophasor, an array of data (dataset) was formed for further processing. Two statistical methods were chosen to study the data and forecast the indicators of electricity quality – the nearest neighbors and ridge regression. With the help of standard Phyton programming language libraries, reading and primary data processing, plotting, statistical processing and implementation of forecasting models were performed. The analysis of the obtained forecast graphs is performed and it is concluded that according to the normalized data the accuracy of the Ridge regression model is higher by 10-15%. The WEB-interface of the system for interactive interaction and visualization of indicators with the output of tables and graphs for analysis, graphical representation and display of the results of diagnostics of electricity quality is designed and developed.