NemoViz: a visual interactive system for atomistic simulations design

Test managers have to repeatedly select test cases for test activities during evolution of large software systems. Researchers have widely studied automated test scoping, but have not fully investigated decision support with human interaction. We previously proposed the introduction of visual analytics for this purpose. Aim: In this empirical study we investigate how to design such decision support. Method: We explored the use of visual analytics using heat maps of historical test data for test scoping support by letting test managers evaluate prototype visualizations in three focus groups with in total nine industrial test experts. Results: All test managers in the study found the visual analytics useful for supporting test planning. However, our results show that different tasks and contexts require different types of visualizations. Conclusion: Important properties for test planning support are: ability to overview testing from different perspectives, ability to filter and zoom to compare subsets of the testing with respect to various attributes and the ability to manipulate the subset under analysis by selecting and deselecting test cases. Our results may be used to support the introduction of visual test analytics in practice.

Massive and complex data impose a challenge on the public health community to explore, analyze, and synthesize valuable information to make timely informed decisions. This study exploits the use of Visual Analytics (VA) to enable health professionals to understand heterogeneous injury data and decide about dynamic health situations. Visual Analytics is defined as the "science of analytical reasoning facilitated by interactive visual interface"[14]. We conducted collaborative Paired and Group Analytics sessions to examine how VA assists health professionals in investigating injury data as well as in supporting knowledge construction and decisionmaking. This manuscript reports how stakeholders perceived VA to be usefulness in helping them understand the injury data, get insights and build knowledge that could potentially prompt actions into critical health situations. Future study implications can inform the design of innovative VA tools and techniques that synthesize novel collaborative VA approaches to optimize the decision-making process.

Visual analytics fundamentally influences the analytical process. Without proper guidance, the use of visual analytics in complex domain can become an obstacle that hides the usefulness of analytical data. Since evaluation is the only way to identify the effectiveness of visual analytics to represent analytical outcomes, misconception of the evaluation process will bury the relevancy of visualization to support valuable decision. Recently, the nature domain of data has changed and we are now dealing with data that is massive, ambiguous, and dynamic, is often processed in real time. Hence, the data is more complex and cognitive activities that visual analytics facilitates are also getting more complex. Thus, this research revisits the way to evaluate visual analytics in complex cognitive conditions that are natural, uncertain and context dependent. Governed by the Design Science Research Methodology (DSRM), there are three phases involve during the evaluation process; i). demonstration, ii). evaluation and iii). communication. The design process is embedded with the human-activity centered design approach to gain better understanding on the visual analytics users and the complex cognitive activities involved. Thus, the research proposes Focus Group Observation method in conducting the evaluation in authentic setting. By offering a set of evaluation recommendations, this research aims to enhance visual analytics among users. It also recommends the evaluation criteria, sampling strategy and participation, focus group tasks

Human-centered learning analytics (HCLA) is an approach that emphasizes the human factors in learning analytics and truly meets user needs. User involvement in all stages of the design, analysis, and evaluation of learning analytics is the key to increase value and drive forward the acceptance and adoption of learning analytics. Visual analytics is a multidisciplinary data science research field that follows a human-centered approach and thus has the potential to foster the acceptance of learning analytics. Although various domains have already made use of visual analytics, it has not been considered much with respect to learning analytics. This paper explores the benefits of incorporating visual analytics concepts into the learning analytics process by (a) proposing the Learning Analytics and Visual Analytics (LAVA) model as enhancement of the learning analytics process with human in the loop, (b) applying the LAVA model in the Open Learning Analytics Platform (OpenLAP) to support human-centered indicator design, and (c) evaluating how blending Learning Analytics and Visual Analytics can enhance the acceptance and adoption of learning analytics, based on the technology acceptance model (TAM).

BackgroundHealth experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making.MethodsHealth expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts’ responses were analysed using statistical and text mining approaches.ResultsThe forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses.ConclusionsThis exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.Graphical abstract

There is limited literature on the value that visual analytics provides for businesses, and its broad use in organisations. This research provides some understanding of how South African businesses are using visual analytics in their day to day operations, and the value derived from employing it. The study was interpretive, exploratory and descriptive, producing both quantitative and qualitative data. Individuals within organisations making use of visual analytics completed an online survey, and interviews were conducted with informed business, IT and BI stakeholders. Results were compared with those from an international survey, and thematic analysis highlighted four main themes: usage, value, challenges and technology. Most respondents noted the high added value obtained from visual analytics versus tables of numbers. The research also identified a set of good practices for organisations to employ when embarking on a visual analytics strategy and suggested ways of mitigating potential challenges.

Background Visual analytics, as a powerful tool, can aid in planning and implementing future changes to the curriculum mapping, thereby ensuring that medical education aligns with the ever-evolving needs of the healthcare system. However, there has been a lack of empirical studies exploring the potential benefits and methodologies of utilizing visual analytics to reshape medical education curricula. The study aimed to describe the use of visual analytics to analyse part of a curriculum for an undergraduate dental program. Methods A qualitative document analysis of the part of a curriculum for the undergraduate dental program at Bakhtawar Amin Medical and Dental College was conducted to identify flaws and propose improvements. The software ATLAS.ti version 9 was used to analyze the relationships among different components of the Behavioral Sciences subject. One-to-one interviews with subject specialists and medical education experts were conducted to gather insights and suggestions for fulfilling the gaps, improved curriculum redesign using visual analytics. Data triangulation was employed to bolster the validity and reliability of the findings. Results The results show eleven different aspects categorized into four major categories, identified after analysis of a part of the curriculum for an undergraduate dental program. ATLAS.ti facilitated the visualization of relationships between designed and delivered curricula including; (i) outcome discrepancies, (ii) alignment of learning outcomes and teaching methodologies, (iii) the relationship between learning outcomes and assessment, and (iv) a comprehensive gap analysis of learning outcomes, teaching methodologies and assessment. Conclusion The findings emphasize the importance of visual analytics in curriculum mapping. The application of visual analytics enables curriculum developers and educators to uncover hidden patterns in educational data and create a well-aligned curriculum that meets the evolving needs of the healthcare system. Further exploration of visual analytics in diverse educational contexts offers promising opportunities for research in the areas of health informatics, information technology, and medical education.

Visual Analytics as an Enabler for Manufacturing Process Decision-making

Introduction

The understanding of medical test results is markedly improved through the use of visual analytics. While traditional theorists in the field of healthcare analytics seek to improve decision making through data mining and machine learning, Visual Analytics augments that intelligence by providing an interface to integrate all of the data in a composite image. KnowYourColors™ is a visual analytics interface providing many dashboards to help the healthcare and insurance providers make better decisions in treatment and spending. This paper discusses many of the visualizations that are used in those applications. This includes new visualizations focused around polar area diagrams that are designed for showing blood metrics and visualizations of prior research work that help in the decision making process. This paper also demonstrates the effectiveness of the application and the reactions of physicians and patients.

To detect harmful medication errors and inform regulatory actions, the U.S. Food & Drug Administration uses the FAERS spontaneous reporting system to collect medication error reports. Drug safety analysts, however, review the submitted report narratives one by one to pinpoint critical medication errors. Based on a formative study of the review process requirements, we design an interactive visual analytics prototype called Medication Error Visual analytics (MEV), to facilitate the medication error review process. MEV visualizes distributions of the reports over multiple data attributes such as products, types of error, etc., to guide analysts towards most concerning medication errors. MEV supports interactive filtering on key data attributes that aim to help analysts hone in on the set of evidential reports. A multi-layer treemap visualizes the count and severity of the errors conveyed in the underlying reports, while the interaction between these layers aid in the analysis of the corresponding data attributes and their relationships. The results of a user study conducted with analysts at the FDA suggests that participants are able to perform the essential screening and review tasks more quickly with MEV and perceive tasks as being easier with MEV than with their existing tool set. Post-study qualitative interviews illustrates analysts’ interest in the use of visual analytics for FAERS reports analysis operations, opportunities for improving the capabilities of MEV, and new directions for analyzing critical spontaneous reports at scale.

Financial institutions handle with hundreds of thousands of wire transactions per day and need to ensure security and quality for their customers. Searching on predefined patterns is insufficient to identify frauds due to continuous evolution of fraudulent methods used by criminals. Systems used for this purpose are based on the application of some methods of Artificial Intelligence, neglect human process analysis and make little use of Visual Analytics (VA) techniques. Frauds detection domain involves time-oriented and multivariate aspects to identify anomalous transactions making fraud detection a difficult task. We propose the creation of a model for each customer based on his/her behavior, using techniques of identification of outliers and conducting analysis through VA to reduce the false positive rate in the identification of fraudulent financial transactions process. We apply this approach to a real Brazilian financial institution with a daily volume of more than 30 million of financial transactions. Our framework includes a hybrid approach: (1) use of unsupervised outlier detection algorithms; and (2) use of VA to support the real time human analysis with the aim of reducing the incidence of false positives. Potential fraudulent information are presented using VA techniques allowing specialists to evaluate suspicious transactions with no increase of the normal processing times. The initial results obtained sign that there are experimental evidence that our approach can overcome the performance of the fraud detection method today used at the Brazilian institution.

Background of Study: Big data visualization and visual analytics are essential in social media analysis because they help process large and complex data into more understandable information. With this technique, we can identify patterns, trends, and relationships in social media data, such as user interactions and social influences, that are difficult to analyze without visualization tools. This allows for faster, more accurate, and in-depth analysis, and helps with data-driven decision-making in a variety of areas. Aims and Scope of Paper: The purpose of this paper is to review the literature related to big data visualization and visual analytics in the context of social media, using bibliometric analysis methods to identify current trends, techniques used, and important tools. Methods: This study used a bibliometric design to analyze the literature in the Scopus database with three main keyword combinations: "Big Data Visualization" AND "Social Media Analysis", "Visual Analytics" AND "Bibliometric Analysis", as well as "Data Visualization" AND "Social Media Analytics". The literature analyzed consisted of journals published between 2015 and 2025. After data collection, filtering is carried out using OpenRefine to eliminate bias and duplication, ensure the accuracy and validity of the data, resulting in objective insights into trends in data visualization and social media analytics. Results: A summary of key findings on the most widely used techniques and tools in big data visualization on social media. Conclusion: Closing on the contribution of bibliometric analysis in understanding the development of big data visualization in social media research.

This study delivers a nuanced and in-depth exploration of multiple risk factors implicated in the onset of hypertension, a pervasive cardiovascular ailment afflicting a staggering number of individuals across the globe. Harnessing an exhaustive, meticulously curated dataset, the research adopts a synergistic, multi-faceted methodology that encompasses both logistic regression models and cutting-edge visual analytics, such as boxplots. Through logistic regression, key predictorsnamely chest pain type, maximum heart rate, and particular outcomes from thallium stress testsemerge as critical determinants in the development of hypertension. To substantiate these statistical inferences, the study leverages a series of illuminating boxplots, offering an intuitive and empirically rigorous portrayal of the stark disparities in these variables between hypertensive and non-hypertensive cohorts. This harmonized, dual-pronged strategy not only quantitatively assesses the impact of each risk factor but also brings credibility to these numerical revelations through its adept use of visual analytics. The robust integration of these analytical approaches bolsters the studys overall reliability, thereby furnishing invaluable, actionable insights for both clinical practitioners and health policymakers.

Until now, little research has been conducted to analyse Twitter conversations about the corporate sponsors of football clubs. The conventional and most widely used method has been to use content analysis to assess the sentiment of the tweets that were sent. However, this approach may be inadequate because sports fans may be unlikely to mention a corporate sponsor in the text they tweet. This study demonstrates the use of visual analytics to assess conversations about corporate sponsors by examining the images people tweet.