Interactive Data Visualization and Dashboards with R

In this study, an experiment is conducted to measure the performance in speed and accuracy of interactive visualizations. A platform for interactive data visualizations was implemented using Django, D3, and Angular. Using this platform, a questionnaire was designed to measure a difference in performance between interactive and noninteractive data visualizations. In this questionnaire consisting of 12 questions, participants were given tasks in which they had to identify trends or patterns. Other tasks were directed at comparing and selecting algorithms with a certain outcome based on visualizations. All tasks were performed on high content screening data sets with the help of visualizations. The difference in time to carry out tasks and accuracy of performance was measured between a group viewing interactive visualizations and a group viewing noninteractive visualizations. The study shows a significant advantage in time and accuracy in the group that used interactive visualizations over the group that used noninteractive visualizations. In tasks comparing results of different algorithms, a significant decrease in time was observed in using interactive visualizations over noninteractive visualizations.

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

Interactive data visualization and dashboards can be an effective way to explore meaningful patterns in large clinical data sets and to inform quality improvement initiatives. However, these interactive dashboards may have usability issues that undermine their effectiveness. These usability issues can be attributed to mismatched mental models between the designers and the users. Unfortunately, very few evaluation studies in visual analytics have specifically examined such mismatches between these two groups. We aimed to evaluate the usability of an interactive surgical dashboard and to seek opportunities for improvement. We also aimed to provide empirical evidence to demonstrate the mismatched mental models between the designers and the users of the dashboard. An interactive dashboard was developed in a large congenital heart center. This dashboard provides real-time, interactive access to clinical outcomes data for the surgical program. A mixed-method, two-phase study was conducted to collect user feedback. A group of designers (N = 3) and a purposeful sample of users (N = 12) were recruited. The qualitative data were analyzed thematically. The dashboards were compared using the System Usability Scale (SUS) and qualitative data. The participating users gave an average SUS score of 82.9 on the new dashboard and 63.5 on the existing dashboard (p = 0.006). The participants achieved high task accuracy when using the new dashboard. The qualitative analysis revealed three opportunities for improvement. The data analysis and triangulation provided empirical evidence to the mismatched mental models. We conducted a mixed-method usability study on an interactive surgical dashboard and identified areas of improvements. Our study design can be an effective and efficient way to evaluate visual analytics systems in health care. We encourage researchers and practitioners to conduct user-centered evaluation and implement education plans to mitigate potential usability challenges and increase user satisfaction and adoption.

Biomedical visualizations are key to accessing biomedical knowledge and detecting new patterns in large datasets. Interactive visualizations are essential for biomedical data scientists and are omnipresent in data analysis software and data portals. Without appropriate descriptions, these visualizations are not accessible to all people with blindness and low vision, who often rely on screen reader accessibility technologies to access visual information on digital devices. Screen readers require descriptions to convey image content. However, many images lack informative descriptions due to unawareness and difficulty writing such descriptions. Describing complex and interactive visualizations, like genomics data visualizations, is even more challenging. Automatic generation of descriptions could be beneficial, yet current alt text generating models are limited to basic visualizations and cannot be used for genomics. We present AltGosling, an automated description generation tool focused on interactive data visualizations of genome-mapped data, created with the grammar-based genomics toolkit Gosling. The logic-based algorithm of AltGosling creates various descriptions including a tree-structured navigable panel. We co-designed AltGosling with a blind screen reader user (co-author). We show that AltGosling outperforms state-of-the-art large language models and common image-based neural networks for alt text generation of genomics data visualizations. As a first of its kind in genomic research, we lay the groundwork to increase accessibility in the field. The source code, examples, and interactive demo are accessible under the MIT License at https://github.com/gosling-lang/altgosling. The package is available at https://www.npmjs.com/package/altgosling.

ABSTRACT: Many companies today utilize interactive data visualization to present accounting information to external users on their investor relations websites and to internal users in applications such as enterprise resource planning, Balanced Scorecard, network security, and fraud detection systems. We develop a taxonomy for examining the current state of interactive data visualization research related to accounting decision making. We organize our review around three themes: the relationship between task characteristics and interactive data visualization techniques, the relationship between decision maker characteristics and interactive data visualization techniques, and the impact of interactive data visualization techniques on decision processes and outcomes. The review categorizes relevant research, describes the research questions addressed, and suggests avenues for further research.

The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis and data visualization. In this contribution, methods for dimensionality reduction from three decades of interdisciplinary research are browsed and their applicability in the above application domains is briefly discussed. Complementing techniques for ensuing interactive data visualization, data navigation and visual exploratory data analysis are presented, which exploit the remarkable human perceptive and associative capabilities for interactive visual exploratory data analysis and systematic recognition system design. The main focus of this paper is on the comparison of feature selection and feature extraction techniques and the potential benefit of their combination. Further, the interesting implications of dimensionality reduction for VLSI design and related area and power consumption are pointed out.

This article presents UCFM, a user-centered developing framework for interactive temporal data visualization. UCFM is comprised mainly by software architecture and software development methods. The software architecture describes modules in interactive temporal data visualization system and their relationships. And based on the software architecture and development practice, the software development methods summarize these specific steps to design and develop interactive temporal data visualization system. To demonstrate UCFM's validity, the development process of an interactive temporal data visualization application is illustrated.

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

In this study, a visual analytics application is put into practice in Swedish secondary school social science classrooms. The application offers support to analyse vast amounts of data through interactive data visualizations. Previous studies have demonstrated that the visual interactive interface challenges the traditional practice in school, where students usually demonstrate their knowledge by means of written texts. Thus, this study examines what happens if students work with more malleable, adaptable, or fluid modes when attempting to express their conclusions from work with interactive data visualizations. It aims to detect patterns in how knowledge visualizations are produced and arranged multimodally. Inspired by design-based research, the study conducted two classroom interventions followed by video captures. It employed a socio-material semiotic approach, which enables the study of interactions between both social and material actors. Three patterns emerged when students’ insights were translated into knowledge visualizations – exploring, gathering, and inserting. It became obvious how different actors taking part of such a digital multimodal writing activity affect and change every actor/everyone/everything, which in turn transfers, relocalizes, reformulates, and re-presents the communicated message. Knowing how knowledge visualizations are produced might strengthen students’ visual abilities when transforming insights multimodally.

<p>Over the last few years, the amount of large and complex data in the public domain has increased enormously and new challenges arose in the representation, analysis and visualization of such data. Considering the number of space missions that provided and will provide remote sensing data, there is still the need of a system that can be dispatched in several remote repositories and being accessible from a single client of commodity hardware.</p><p>To tackle this challenge, at the DLR Institute for Software Technology we have defined a dual backend frontend system, enabling the interactive analysis and visualization of large-scale remote sensing data. The basis for all visualization and interaction approaches is CosmoScout VR, a visualization tool developed internally at DLR, and publicly available on Github, that allows the visualization of complex planetary data and large simulation data in real-time. The dual component of this system is based on an MPI framework, called Viracocha, that enables the analysis of large data remotely, and allows the efficient network usage about sending compact and partial results for interactive visualization in CosmoScout as soon as they are computed.</p><p>A node-based interface is defined within the visualization tool, and this lets a domain expert to easily define customized pipelines for processing and visualizing the remote data. Each “node” of this interface is either linked with a feature extraction module, defined in Viracocha, or to a rendering module defined directly in CosmoScout. Being this interface completely customizable by a user, multiple pipelines can be defined over the same dataset to enhance even more the visualization feedback for analysis purposes.</p><p>Being an ongoing project, on top of these tools, as a novel strategy in EO data processing and visualization, we plan to define and implement strategies based on Topological Data Analysis (TDA). TDA is an emerging set of technique for processing the data considering its topological features. These include both the geometric information associated to a point, as well all the non-geometric scalar values, like temperature and pressure, to name a few, that can be captured during a monitoring mission. One of the major theories behind TDA is Discrete Morse Theory, that, given a scalar value, is used to define a gradient on such function, extract the critical points, identify the region-of-influence of each critical point, and so on. This strategy is parameter free and enables a domain scientist to process large datasets without a prior knowledge of it.</p><p>An interesting research question, that it will be investigated during this project is the correlation of changes of critical points at different time steps, and the identification of deformation (or changes) across time in the original dataset.</p>

Interactive 3D data visualization plays a key role in HEP experiments, as it is used in many tasks at different levels of the data chain. Outside HEP, for interactive 3D graphics, the game industry makes heavy use of so-called “game engines”, modern software frameworks offering an extensive set of powerful graphics tools and cross-platform deployment. Recently, a very strong support for Virtual Reality (VR) technology has been added to such engines. In this talk we explore the usage of game engines and VR for HEP data visualization, discussing the needs, the challenges and the issues of using such technologies. We will also make use of ATLASrift, a VR application developed by the ATLAS experiment, to discuss the lessons learned while developing it using the game engine Unreal Engine, and the feedback on the use of Virtual Reality we got from users while using it at many demonstrations and public events.

Data visualization has been widely utilized in various scenarios in data analytics for business purposes, especially helping novice readers make sense of complex dataset with interactive functions. However, due to an insufficient theoretical understanding of the process of developing interactive functions and visual presentations, interactive data visualization tools often display all available automatic graphing functions in front of users, instead of guiding them to choose a visualization based on their demands. Thus, this paper is intended to construct a process of developing interactive visualization with a specific focus on enabling the interoperation between design and interpretation. Stemmed from organizational semiotics, an abductive process will be portrayed in this paper to interpret the process of developing interactive data visualization. Especially the interactive functions will be employed in an iterative process, where producers can be aware of and respond to readers’ information demands on semantic, pragmatic and social levels.

Interactive 3D visualizations of geospatial data are currently available and popular through various applications such as Google EarthTM and others. Several studies have focused on user performance with 3D maps, but static 3D maps were mostly used as stimuli. The main objective of this paper was to identify differences between interactive and static 3D maps. We also explored the role of different tasks and inter-individual differences of map users. In the experimental study, we analyzed effectiveness, efficiency, and subjective preferences, when working with static and interactive 3D maps. The study included 76 participants and used a within-subjects design. Experimental testing was performed using our own testing tool 3DmoveR 2.0, which was based on a user logging method and open web technologies. We demonstrated statistically significant differences between interactive and static 3D maps in effectiveness, efficiency, and subjective preferences. Interactivity influenced the results mainly in ‘spatial understanding’ and ‘combined’ tasks. From the identified differences, we concluded that the results of the user studies with static 3D maps as stimuli could not be transferred to interactive 3D visualizations or virtual reality.

This work presents a new interactive data visualization approach based on mixture of the outcomes of dimensionality reduction (DR) methods. Such a mixture is a weighted sum, whose weighting factors are defined by the user through a visual and intuitive interface. Additionally, the low-dimensional representation space produced by DR methods are graphically depicted using scatter plots powered via an interactive data-driven visualization. To do so, pairwise similarities are calculated and employed to define the graph to be drawn on the scatter plot. Our visualization approach enables the user to interactively combine DR methods while provided information about the structure of original data, making then the selection of a DR scheme more intuitive.

The purpose of this study was to develop an interactive data visualization platform for evaluating trabeculectomy outcomes and monitoring clinical care quality. This is a retrospective electronic health record study in a glaucoma division of an academic medical center. We included 633 patients who underwent trabeculectomy between July 2016 and December 2018. Data were collected for intraocular pressure (IOP), vision loss, number of follow-up visits, and return to the operating room within the initial 3 months. Regression analyses were used to assess factors associated with these study outcomes. Risk of IOP ≤5 mm Hg was assessed using survival analysis. An interactive data visualization platform was created. A total of 429 trabeculectomy and 204 combined surgeries were analyzed, with preoperative IOP of 21.9±9.4 and 19.4±6.9 mm Hg, respectively. Mean pressure declined by 53% and 41% (P=0.001) by 3 months. Risk of pressure ≤5 mm Hg was higher in white patients and those who underwent trabeculectomy alone. At 3 months, patients on average had 5 clinic visits; 5.5% required additional surgical procedures, and 18% experienced loss of at least 3 lines of best-recorded (not best-corrected) vision. Returning to the operating room was significantly associated with an increased number of clinic visits (coefficient=3.94) and higher odds of vision loss (odds ratio=11.12). Over the initial 3 months after trabeculectomy, additional surgeries are rare, while a significant proportion of patients lost vision, and this was strongly associated with a need to return to the operating room. Interactive electronic health record data visualization can be a useful tool for monitoring clinical outcomes.