Visual Analytics Tool Research Articles

Analysis of the research status, hot spots and trends of domestic cross-border e-commerce platforms-- Scientific knowledge graph analysis based on Cite Space

Behind the good development of cross-border e-commerce, cross-border e-commerce platform plays an important role.The number of relevant research literatures based on cross-border e-commerce platform is too large and the information is too complex. It is difficult to analyze and study comprehensively and accurately by using traditional data analysis methods. Therefore, the author uses bibliometric analysis, quantitative visualization analysis and Cite Space literature visualization analysis tools. To make a visual analysis of the current situation, hot spots and trends of cross-border e-commerce platforms.The analysis results show that the academic research in this field is in a stable development stage, and "supply chain", "block chain", "global value chain", "brand internationalization" and other hot topics.Newly added keywords include "platform empowerment", "cross-border logistics", "digital economy", "brand management", etc."Global value chain", "platform empowerment", "digital economy", "block chain", etc., will become the future research trends in the field of cross-border e-commerce platforms.The author will sort out and analyze the research results, so as to facilitate subsequent researchers to intuitively understand the research status, hot spots and trends of cross-border e-commerce platforms, and provide reference and basis for subsequent theoretical research and practical activities.

Analysis of the research status, hot spots and trends of domestic cross-border e-commerce platforms-- Scientific knowledge graph analysis based on Cite Space

Behind the good development of cross-border e-commerce, cross-border e-commerce platform plays an important role.The number of relevant research literatures based on cross-border e-commerce platform is too large and the information is too complex. It is difficult to analyze and study comprehensively and accurately by using traditional data analysis methods. Therefore, the author uses bibliometric analysis, quantitative visualization analysis and Cite Space literature visualization analysis tools. To make a visual analysis of the current situation, hot spots and trends of cross-border e-commerce platforms.The analysis results show that the academic research in this field is in a stable development stage, and "supply chain", "block chain", "global value chain", "brand internationalization" and other hot topics.Newly added keywords include "platform empowerment", "cross-border logistics", "digital economy", "brand management", etc."Global value chain", "platform empowerment", "digital economy", "block chain", etc., will become the future research trends in the field of cross-border e-commerce platforms.The author will sort out and analyze the research results, so as to facilitate subsequent researchers to intuitively understand the research status, hot spots and trends of cross-border e-commerce platforms, and provide reference and basis for subsequent theoretical research and practical activities.

ITalk–iSee: A participatory visual learning analytical tool for productive peer talk

iTalk–iSee: A participatory visual learning analytical tool for productive peer talk

Assessment of “The Serious Learner” Outdoor Concrete Statue in TTU, Ghana: A Restoration Process (Before and After)

This paper assesses the restoration process (before and after) of "The Serious Learner”, an outdoor concrete statue found at the forecourt of Oduro block, Takoradi Technical University (TTU)-Ghana. Direct observations and unstructured interviews under the qualitative research method were employed to elicit data from respondents such as art lecturers and students of the Sculpture Department, TTU. Data were analyzed using archival studies, descriptive and visual analysis tools with a studio-based process. The study revealed that "The Serious Learner" outdoor concrete statue formed an integral element of landscaping at the Oduro block, TTU. However, significant features such as the pedestal, body parts, lighting system and finishes of the outdoor statue have deteriorated due to weathering and had received no restoration since its erection. Repairs and management of artefacts in public space, not only in TTU, should be performed every two years to ensure their longevity and posterity as conservation and restoration method is significant in outdoor sculptures.

Leveraging Explainable AI to Support Cryptocurrency Investors

In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool.

Interactive optimization of embedding-based text similarity calculations

Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.

Activity and Dropout Tracking in Moodle Using UBUMonitor Application

Teaching with online learning platforms should simplify the monitoring of students’ activity, particularly when evaluating student dropout. Popular learning environments such as Moodle should implement visual analytic tools that facilitate such tasks, nevertheless, institutions are usually reluctant to incorporate them. This paper presents UBUMonitor, a desktop application that allows the visualization of student’s activity data, extended as a proof of concept with a module for dropout tracking. Therefore, by using UBUMonitor, teachers will be able to easily visualize their students’ engagement with their subject, which can facilitate early action to prevent students from dropping out more effectively.

SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Visual learning analytics (VLA) tools and technologies enable the meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow a better understanding of students’ educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher’s purpose. In this study, we conducted a user study and improved our web-based similarity-based grouping VLA tool, (SBGTool) to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms, and improvement in some visualizations. To counteract color blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the numbers of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students’ engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student–system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool’s efficacy. Finally, we provide the outcomes of the user study that evaluated the tool’s effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study.

BackgroundScientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, thus hindering our ability to address some critical questions.ObjectiveThis study aims to answer the following questions: To what extent can secondary data analytics tools facilitate the generation of scientific hypotheses during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? Furthermore, this study explores the process of scientific hypothesis generation in the context of clinical research. It was designed to compare the role of VIADS, a visual interactive analysis tool for filtering and summarizing large data sets coded with hierarchical terminologies, and the experience levels of study participants during the scientific hypothesis generation process.MethodsThis manuscript introduces a study design. Experienced and inexperienced clinical researchers are being recruited since July 2021 to take part in this 2×2 factorial study, in which all participants use the same data sets during scientific hypothesis–generation sessions and follow predetermined scripts. The clinical researchers are separated into experienced or inexperienced groups based on predetermined criteria and are then randomly assigned into groups that use and do not use VIADS via block randomization. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale survey. A panel of clinical research experts will assess the scientific hypotheses generated by participants based on predeveloped metrics. All data will be anonymized, transcribed, aggregated, and analyzed.ResultsData collection for this study began in July 2021. Recruitment uses a brief online survey. The preliminary results showed that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they used VIADS or other analytics tools. A metric to more accurately, comprehensively, and consistently assess scientific hypotheses within a clinical research context has been developed.ConclusionsThe scientific hypothesis–generation process is an advanced cognitive activity and a complex process. Our results so far show that clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience. However, refining these scientific hypotheses is a much more time-consuming activity. To uncover the fundamental mechanisms underlying the generation of scientific hypotheses, we need breakthroughs that can capture thinking processes more precisely.International Registered Report Identifier (IRRID)DERR1-10.2196/39414

The Use of Android Game to Improve Impaired Hearing Students Vocabulary Mastery

This study aims to analyze the use of android game to improve impaired hearing students vocabulary mastery at one of Special Schools in Padang City, Indonesia. This study used quantitative research method with Single Subject Research (SRR) approach, the research data was taken from one impaired hearing student through seventeen direct lessons using android game. All the data that the authors obtained were analyzed descriptively using visual graph analysis tools, this method of analysis is one of the techniques for analyzing Single Subject Research. The results of the study showed that the use of Android game as a learning media significantly improved the impaired hearing student vocabulary mastery. This increase in ability is proved by the initial test scores of the participants at a score of 10, 10, 20, 20, 20, this score is the score before learning by using android game. Furthermore, after the author did seven times direct treatment, it was found that the students’ scores increased to 40, 50, 70, 80, 70, 70, 70, even students were able to obtain final self-evaluation scores with a score of 50, 70, 100, 100, 100. The results of this study proved that the use of android gamehas succeeded in increasing the vocabulary mastery of impaired hearing students

Building International Capacity for Citizen Scientist Engagement in Mosquito Surveillance and Mitigation: The GLOBE Program's GLOBE Observer Mosquito Habitat Mapper.

Simple SummaryThe GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a free citizen science data collection tool that can be downloaded onto smartphones. The Mosquito Habitat Mapper encourages individuals to participate in locating and removing mosquito breeding habitats from use. An easy-to-use graphic interface enables users to report and describe mosquito breeding habitats, places with standing water where immature mosquitoes grow and develop. Citizen scientists are asked to determine if they see immature mosquitoes and if they wish to count and identify any mosquito larvae they see. In the last task, the user is asked to dump out or cover the standing water source, eliminating its use as a breeding habitat. In this way, the GLOBE Observer mobile app also supports the actions of individuals protecting their communities from mosquito-borne disease. In addition, all data reported by citizen scientists are publicly available. Scientists are accessing this data for a variety of research uses, including the development of automated techniques to recognize larvae and mosquito breeding sites from digital images. Since 2017, more than 32,000 Mosquito Habitat Mapper observations have been submitted by citizen scientists in 84 countries.The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a no-cost citizen scientist data collection tool compatible with Android and iOS devices. Available in 14 languages and 126 countries, it supports mosquito vector surveillance, mitigation, and education by interested individuals and as part of participatory community surveillance programs. For low-resource communities where mosquito control services are inadequate, the Mosquito Habitat Mapper supports local health action, empowerment, and environmental justice. The tangible benefits to human health supported by the Mosquito Habitat Mapper have encouraged its wide adoption, with more than 32,000 observations submitted from 84 countries. The Mosquito Habitat Mapper surveillance and data collection tool is complemented by an open database, a map visualization interface, data processing and analysis tools, and a supporting education and outreach campaign. The mobile app tool and associated research and education assets can be rapidly deployed in the event of a pandemic or local disease outbreak, contributing to global readiness and resilience in the face of mosquito-borne disease. Here, we describe the app, the Mosquito Habitat Mapper information system, examples of Mosquito Habitat Mapper deployment in scientific research, and the outreach campaign that supports volunteer training and STEM education of students worldwide.

VAST 2020 Contest Challenge: GraphMatchMaker-Visual Analytics for Graph Comparison and Matching.

We report on the process and design of our visual analytics graph analysis challenge winning entry. Specifically, our team addressed the IEEE VAST 2020 Mini-Challenge 1 that asked participants to identify a group of people that accidentally caused an internet outage. To identify this group, we were given a network profile and a large multivariate social network to search in. Our approach involved statistical and graphical analysis as well as the design of three custom visual analytics tools. The submitted solution and visualizations are available at https://graphletmatchmaker.github.io/.

Bulsarapp: Interactive Visual Analysis for Surname Trend Exploration.

The study of surnames for a given population, together with their distribution and spatial patterns identification, has been a long-standing problem in the fields of human biology, public health, and social sciences. The ancestry inferred from surname information can be a useful means to understand the dynamics of human populations. This knowledge allows us to characterize geographically the ethnicity of populations, and to understand the complex relationships between identity, migration, and health issues in a demographic view. However, in most cases, a detailed geolocalization of this data can be a daunting task. We propose a visual analytic tool that summarizes the heterogeneous surname and geographic information collected from Argentinean electoral rolls. This tool allows a massive data analysis, and facilitates interdisciplinary studies about population dynamics related to ancestry, migration, and health. It also offers an easy-to-use interface that allows interactive exploration of isonymy and surname origins, their distribution, and spatial trends in a high population density context.

Multifaceted Visual Analysis of Oceanographic Simulation Ensemble Data.

The analysis of multirun oceanographic simulation data imposes various challenges ranging from visualizing multifield spatio-temporal data over properly identifying and depicting vortices to visually representing uncertainties. We present an integrated interactive visual analysis tool that enables us to overcome these challenges by employing multiple coordinated views of different facets of the data at different levels of aggregation.

Community engaged tick surveillance and tickMAP as a public health tool to track the emergence of ticks and tick-borne diseases in New York.

A community engaged passive surveillance program was utilized to acquire ticks and associated information throughout New York state. Ticks were speciated and screened for several tick-borne pathogens. Of these ticks, only I. scapularis was commonly infected with pathogens of human relevance, including B. burgdorferi, B. miyamotoi, A. phagocytophilum, B. microti, and Powassan virus. In addition, the geographic and temporal distribution of tick species and pathogens was determined. This enabled the construction of a powerful visual analytical mapping tool, tickMAP to track the emergence of ticks and tick-borne pathogens in real-time. The public can use this tool to identify hot-spots of disease emergence, clinicians for supportive evidence during differential diagnosis, and researchers to better understand factors influencing the emergence of ticks and tick-borne diseases in New York. Overall, we have created a community-engaged tick surveillance program and an interactive visual analytical tickMAP that other regions could emulate to provide real-time tracking and an early warning for the emergence of tick-borne diseases.

Expressive querying for accelerating visual analytics

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and time-consuming. In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching. To address them, we first present our work on Zenvisage that helps enumerate large collections of visualizations and supports simple visualization matching with the help of an interactive interface and an expressive visualization query language. For more finegrained and flexible visualization matching, including search for underspecified and approximate patterns, we extend Zenvisage to develop ShapeSearch. ShapeSearch supports a novel shape querying algebra that helps express a large class of pattern queries that are hard to specify with existing systems. ShapeSearch exposes multiple specification mechanisms: sketch, natural-language, and visual regular expressions that help users easily issue shape queries, while applying query-aware and perceptually-aware optimizations to efficiently execute them within interactive response times. To conclude, we discuss a number of open research problems to further improve the usability and performance of both Zenvisage and ShapeSearch.

Actor-Focused Interactive Visualization for AI Planning

As we grow more reliant on AI systems for an increasing variety of applications in our lives, the need to understand and interpret such systems also becomes more pronounced, be it for improvement, trust, or legal liability. AI Planning is one type of task that provides explanation challenges, particularly due to the increasing complexity in generated plans and convoluted causal chains that connect actions and determine overall plan structure. While there are many recent techniques to support plan explanation, visual aids for navigating this data are quite limited. Furthermore, there is often a barrier between techniques focused on abstract planning concepts and domain-related explanations. In this paper, we present a visual analytics tool to support plan summarization and interaction, focusing in robotics domains using an actor-based structure. We show how users can quickly grasp vital information about actions involved in a plan and how they relate to each other. Finally, we present a framework used to design our tool, highlighting how general PDDL elements can be converted into visual representations and further connecting concept to domain.

Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies.

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution–solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.

Understanding the Imperfection of 3D point Cloud and Semantic Segmentation algorithms for 3D Models of Indoor Environment

Abstract. Point clouds data provides new potentials for automated construction of more geometrically accurate and semantically rich 3D models for indoor environments. Recent advances in deep learning methods on point cloud semantic segmentation demonstrated impressive accuracy in labeling points of 3D surfaces with object classes. However, it remains challenging to reconstruct the shape of semantic objects from semantically-labeled 3D points, due to imperfection of such data and the under-determination of object construction algorithms. We have little empirical knowledge about how data imperfections affect the reconstruction of 3D indoor room objects. This paper contributes to understanding the nature of such imperfection of 3D point cloud data and semantic segmentation algorithms by analyzing the reconstructability of indoor room objects from semantically-labeled point cloud. 181 rooms from Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS) were used in our experiment. After generating semantic labels on point-clouds using PointNet++ segmentic segmentation algorithm, we use human coders to judge the reconstructability of indoor objects, following a qualitative coding scheme. Human exploration of object shape imperfection was assisted by a visual analytic tool in making their judgement. We found that high point-level accuracy achieved through semantic segmentation of point cloud data does not guarantee high object-level accuracy. The extent of this problem varies widely among different spatial settings and configurations. We discuss the significance of these findings on the choice of 3D reconstruction methods.

A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools.