Information Visualization Approach Research Articles

PIXEL-ORIENTED VISUALIZATION FOR EXPLAINING DATA CLASSIFICATION IN A MULTILAYER NEURAL NETWORK

Accompanying the growth of applications that use Artificial Intelligence, recent research is also growing to explain how these applications work and make them more acceptable to humans. In this context, this paper presents an alternative explanation of the data classification process carried out by an Artificial Intelligence algorithm. The work proposes a pixel-oriented information visualization approach to explain the multilayer perceptron classifier using SHAP. Observing the results obtained in Shapley values, for the Iris dataset composed of four features, the proposed methodology identified one relevant feature, and for the Wine dataset composed of 13 features, the methodology identified six relevant features. The relevant features are those that most influence the classification, this information explains the results as it is possible to understand the reasons for the classifier's successes and errors.

Heatmap Visualization Approach for Descriptive Analytics of Covid-19 Cases in Malaysia

The Covid-19 pandemic has presented unparalleled obstacles to the world, including Malaysia and Indonesia. A visual analytics approach known as the Covid-19 heatmap visualization is proposed to provide relevant and timely information about Covid-19 daily cases and deaths in Malaysia. The aim of this approach is to offer a platform for various stakeholders to make informed decision based on descriptive analytics. The Covid-19 information visualization approach allows users to compare different types of visualizations such as heatmaps, line charts, and bar charts to detect Covid-19 trends or patterns. The approach is implemented as a web-based system using D3 JavaScript library and tested using the data collected from Coronavirus Pandemic Data Explorer. Results show that the Covid-19 heatmap visualization approach is an invaluable approach for understanding pandemic trends as it provides users with easy access to visualizations and fast understanding of Covid-19 situations such as daily cases, deaths and recoveries. The target users for the proposed approach are the National Security Council and Ministry of Health staff, company crews, and the general public. Future upgrades to the approach includes adding additional attributes such as the number of tests and recoveries to assist users in making predictive analytics using machine learning models.

Mental Modeling of Entrepreneurial Opportunity Based on the Principle of Information Visualization

What arouses entrepreneurial action? The role of entrepreneurial opportunities in stimulating entrepreneurial action under uncertainty is a subject of intense scholarly debate. This article engages in a crucial theoretical exploration to extend the boundaries of opportunity by reframing it as an artifact. Reframing the opportunity as an artifact (crystallized information) shifts the dialogue and perspectives. Through information visualization and mental modeling approaches, information is aggregated, integrated, and configured to generate an experience that triggers actions. The interaction, transformation, and representation of information are intended to reduce uncertainty and guide entrepreneurial enactment. This article presents an integrated information processing model showing how information excites, instigates, and motivates enactment. This contextualization aims to develop a descriptively accurate and prescriptively useful concept of opportunity as an artifact. Theoretically, analytically, and conceptually, the focus on information as the fundamental unit of analysis shifts the discussion by featuring inward to move the scholarly debate on opportunity forward. This re-pivoting can avoid the calls for abandonment and debunking of the opportunity construct.

AN INNOVATIVE DATA-DRIVEN FALL PREVENTION COMMUNICATION TOOL FOR ADMINISTRATORS, NURSE MANAGERS, AND STAFF NURSES

Falls are the leading cause of injury among older adults, resulting in 3 million emergency department visits and 800,000 hospitalizations each year in the United States alone. In the hospital setting, falls are among the most common adverse events, causing longer stays and higher costs of care. Substantial efforts have been made to reduce falls in the past decade but with limited sustained effect. This may in part be due to limitations of existing tools for fall-risk assessment and evidence-based fall prevention. Therefore, there is a need to strengthen the evidence on risk factors for hospital-acquired falls and address barriers to translating the best available evidence into practice. To this end, we undertook the development of an innovative dissemination tool to implement fall-related evidence generated through state-of-the-art data science and information visualization approaches. Through a multidisciplinary academic-clinical partnership, we have developed an infographic to disseminate empirical evidence to nurses and administrators in the hospital setting. The infographic was developed based on principles of user-centered design and persuasive communication, and focuses on providing clear and accessible information about factors contributing to a patients’ risk of falling in the hospital. This innovative dissemination approach is intended to foster dialogue between administrators, mid-level nursing management and staff nurses as well as evidence-based practice at the bedside. Future use and evaluation of this fall prevention tool will focus on adapting the infographic as an interactive digital tool for education and engagement of patients and families in the hospital setting.

Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges.

After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.

A collaborative, ontological and information visualization model approach in a centralized rock art heritage platform

The Rock-Art Database is a rock art heritage project based in the Place, Evolution and Rock Art Heritage Unit (PERAHU), School of Humanities, Languages and Social Science, Griffith University, Queensland, Australia that began in 2012. The project aims to bring members of the global rock art community together in one centralized online platform to share and discuss rock art information in order to make rock art data more accessible and more visible. The platform offers a tool to a wide range of users to collect, manage and disseminate rock art data through specialized functionalities in digital formats that explore new ways to look at rock art through a collaborative, ontological and information visualization approach. The Rock-Art Database was first released in 2014 and can be accessed on the Web at http://www.rockartdatabase.com. This paper presents results of the deployed Rock-Art Database model and discusses its potential for future rock art data explorations.

Toward exploratory analysis of diversity unified across fields of study: an information visualization approach

The study of the diversity of multivariate objects shares common characteristics and goals across disciplines, including ecology and organizational management. Nevertheless, subject-matter experts have adopted somewhat separate diversity concepts and analysis techniques, limiting the potential for sharing and comparing across disciplines. Moreover, while large and complex diversity data may benefit from exploratory data analysis, most of the existing techniques emphasize confirmatory analysis based on statistical metrics and models. This work aims to bridge these gaps. First, by cross comparing the analyses of species diversity, microbial diversity, and workgroup diversity, we introduce a framework of diversity concerns aligned across the three areas. The alignment framework is validated and refined by feedback from subject-matter experts. Then, guided by the framework and theoretical information visualization and visual analytics principles (as distinguished from scientific visualization), we propose a unified taxonomy of common analytical tasks for exploration of diversity.

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

An overview is provided of the various methods for analyzing biosensing data, with emphasis on information visualization approaches such as multidimensional projection techniques. Emphasis is placed on the importance of data analysis methods, with a description of traditional techniques, including the advantages and limitations of linear and non-linear methods to generate layouts that emphasize similarity/dissimilarity relationships among data instances. Particularly important are recent methods that allow processing high-dimensional data, thus taking full advantage of the capabilities of modern equipment. In this area, now referred to as e-science, the choice of appropriate data analysis methods is crucial to enhance the sensitivity and selectivity of sensors and biosensors. Two types of systems deserving attention in this context are electronic noses and electronic tongues, which are made of sensor arrays whose electrical or electrochemical responses are combined to provide "finger print" information for aromas and tastes. Examples will also be given of unprecedented detection of tropical diseases, made possible with the use of multidimensional projection techniques. Furthermore, ways of using these techniques along with other information visualization methods to optimize biosensors will be discussed.

An information visualization approach to improving data quality

An information visualization approach to improving data quality

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the flexibility of a VA approach in the analysis of biological datasets. Examples of these datasets in immunology include flow cytometry, Luminex data, and genotyping (e.g., single nucleotide polymorphism) data. Contrary to the traditional information visualization approach, VA restores the analysis power in the hands of analyst by allowing the analyst to engage in real-time data exploration process. We selected the VA software called Tableau after evaluating several VA tools. Two types of analysis tasks analysis within and between datasets were demonstrated in the video presentation using an approach called paired analysis. Paired analysis, as defined in VA, is an analysis approach in which a VA tool expert works side-by-side with a domain expert during the analysis. The domain expert is the one who understands the significance of the data, and asks the questions that the collected data might address. The tool expert then creates visualizations to help find patterns in the data that might answer these questions. The short lag-time between the hypothesis generation and the rapid visual display of the data is the main advantage of a VA approach.

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Visual analytics (VA) has emerged as a new way to analyze large dataset through interactive visual display. We demonstrated the utility and the flexibility of a VA approach in the analysis of biological datasets. Examples of these datasets in immunology include flow cytometry, Luminex data, and genotyping (e.g., single nucleotide polymorphism) data. Contrary to the traditional information visualization approach, VA restores the analysis power in the hands of analyst by allowing the analyst to engage in real-time data exploration process. We selected the VA software called Tableau after evaluating several VA tools. Two types of analysis tasks analysis within and between datasets were demonstrated in the video presentation using an approach called paired analysis. Paired analysis, as defined in VA, is an analysis approach in which a VA tool expert works side-by-side with a domain expert during the analysis. The domain expert is the one who understands the significance of the data, and asks the questions that the collected data might address. The tool expert then creates visualizations to help find patterns in the data that might answer these questions. The short lag-time between the hypothesis generation and the rapid visual display of the data is the main advantage of a VA approach.

Library and information science (LIS) as we see it: An overview at the state and country level from 1965-2010

This study is motivated to explore and visualize the dynamic citation impact at the state and country level. We present several information visualization approaches, including cartograms and global citation maps, to understand the citation impact distribution in library and information science (LIS). We find that the citation impact in LIS is highly skewed towards North American and European countries. Through the dynamic analysis, states such as GEORGIA and FLORIDA, countries such as CHINA and SOUTH KOREA are becoming more impactful.

Embodied cognition of information visualization: Human–computer interaction with six degrees of freedom in movement of an information space

Mental healthcare has been identified as one of the major social and economic challenges that society will face in the coming decades. In this paper, we explore the potential of technology to improve people’s access to and engagement in, as well as the effectiveness and affordability of, mental healthcare services while interacting with computers. Information visualization plays a crucial role in people’s interaction with computers. Specifically, information visualization represents data or concepts graphically and helps people construct cognitive maps i.e., mental representations of the information space. Well-designed information visualization methods enable users to employ their mental capabilities to manipulate an information space and perceive it based on good mental health. According to the latest study of mental capability, movement will guide insight in problem solving situations. Movement in the 3D world is a crucial part of our mental capability, but traditional information visualization approaches fail to consider it. In an attempt to fill this research gap, we propose a new information visualization method that integrates a user’s visual perception and his/her ergonomic perception. Specifically, the approach enables users to manipulate an information space to follow the behavior of human movement in the 3D world with six degrees of freedom. Users can move an information space along the x-, y-, and z-axes as well as rotate it-giving six degrees of freedom. Manipulating the information space in this way provides users with a more comprehensive understanding of the space, and integrates their visual capability and ergonomic perception so that they can fully utilize their mental capabilities.

A Magic Treemap Cube for Visualizing Olympic Games Data

Treemap, a common information visualization approach for dividing a display area into nested rectangles, has a good performance on visualizing hierarchical data. And it enables user to compare nodes in the same level and to learn certain relationship between adjacent levels. However, treemap layout is limited to visualize multi-dimensional hierarchical data in which the hierarchy is unfixed and complex, especially when the hierarchy is an arbitrary graph. Considering this problem, we present a novel extension called magic treemap cube, which improves the layout and has a good performance on visualizing unfixed (cross-level) and complex data, inverting hierarchy without reorganizing the structure, and enabling users to easily compare among treemaps. The magic treemap cube is a three-dimensional regular hexahedron whose six faces are covered by six different treemaps. Each dimension of the magic treemap cube is a hierarchy structure specified by users according to a hierarchical division of visualization information. With the treemap algorithm, tagcloud and mapping rules, the treemaps are automatically controlled by permutations of three dimensions of the cube. Users could observe the six correlative treemaps respectively by rotating the magic treemap cube in three dimensional Cartesian coordinate. And this magic treemap cube can be unfolded into a tiled treemap in 2D Cartesian coordinate for users-friendly comparison of each treemap. Meanwhile, a merging operation to the tiled treemap is proposed to not only compare two treemaps through the adjacency relations among faces but also provide more information. For demonstrating the above characteristics, an application (called OlympicVis) has been implemented to visualize the Beijing 2008 Olympic Games information, which involves multi-dimensional hierarchy and interconnected data. The OlympicVis can display Cube View, Schedule View, Medal View, Date Filter and Time Line.

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less destabilizing and more effective type of therapy than the chemotherapy-based ones. The advancements brought by Visual MRI are two: first, it is an integration of effective information visualization (IV) techniques into a clustering framework, which separates each MRI image in a set of informative clusters; the second improvement relies in the clustering framework itself, which is derived from a recently re-discovered non-parametric grouping strategy, i.e., the mean shift. The proposed methodology merges visualization methods and data mining techniques, providing a computational framework that allows the physician to move effectively from the MRI image to the images displaying the derived parameter space. An unsupervised non-parametric clustering algorithm, derived from the mean shift paradigm, and called MRI-mean shift, is the novel data mining technique proposed here. The main underlying idea of such approach is that the parameter space is regarded as an empirical probability density function to estimate: the possible separate modes and their attraction basins represent separated clusters. The mean shift algorithm needs sensibility threshold values to be set, which could lead to highly different segmentation results. Usually, these values are set by hands. Here, with the MRI-mean shift algorithm, we propose a strategy based on a structured optimality criterion which faces effectively this issue, resulting in a completely unsupervised clustering framework. A linked brushing visualization technique is then used for representing clusters on the parameter space and on the MRI image, where physicians can observe further anatomical details. In order to allow the physician to easily use all the analysis and visualization tools, a visual interface has been designed and implemented, resulting in a computational framework susceptible of evaluation and testing by physicians. Visual MRI has been adopted by physicians in a real clinical research setting. To describe the main features of the system, some examples of usage on real cases are shown, following step by step all the actions scientists can do on an MRI image. To assess the contribution of Visual MRI given to the research setting, a validation of the clustering results in a medical sense has been carried out. From a general point of view, the two main objectives reached in this paper are: (1) merging information visualization and data mining approaches to support clinical research and (2) proposing an effective and fully automated clustering technique. More particularly, a new application for MRI data analysis, named Visual MRI, is proposed, aiming at improving the support of medical researchers in the context of cancer therapy; moreover, a non-parametric technique for cluster analysis, named MRI-mean shift, has been drawn. The results show the effectiveness and the efficacy of the proposed application.

Towards a methodology for developing visualizations

This paper presents a case study of the development of a visualization to represent and explore the relationships between multiple hierarchical structures, specifically botanical taxonomies. The case study outlines the visualization's development from initial meetings with taxonomists, through the early visual sketches of their activities, and through to the prototype as it exists now after two rounds of usability testing. Qualitative user feedback was elicited from taxonomists who used the system, using standard techniques, taken from traditional usability methodologies such as direct observation, concurrent verbal protocols, video recording and software logs. Some difficulties and differences in the testing and stages of development arising from the information visualization (IV) approach to the graphical display, as contrasted to a more standard graphical user interface (GUI), are noted and solutions proposed.