Data Visualization Tools Research Articles

A novel approach to patient portal activation data to power equity improvements.

There are significant disparities in access and utilization of patient portals by age, language, race, and ethnicity. We developed ambulatory and inpatient portal activation equity dashboards to understand disparities in initial portal activation, identify targets for improvement, and enable monitoring of interventions over time. We selected key metrics focused on episodes of care and filters to enable high-level overviews and granular data selection to meet the needs of health system leaders and individual clinical units. In addition to highlighting disparities by age, preferred language, race and ethnicity, and insurance payor, the dashboards enabled development and monitoring of interventions to improve portal activation and equity. Data visualization tools that provide easily accessible, timely, and customizable data can enable a variety of stakeholders to understand and address healthcare disparities, such as patient portal activation. Further institutional efforts are needed to address the persistent inequities highlighted by these dashboards.

Enhancing decision‐making in higher education: Exploring the integration of ChatGPT and data visualization tools in data analysis

AbstractThis chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.Practical Takeaways The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential. Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders. Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies. Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits. ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks.

Developing Dashboard Analytics and Visualization Tools for Effective Performance Management and Continuous Process Improvement

Performance management and continuous process improvement are essential for businesses to monitor how well their operations are running, find ways to improve them, and bring about good change. However, making decisions based on data in these areas can be challenging because performance data is complicated. This research looks into how panel analytics and data visualization tools can help manage performance well and make processes better all the time. A literature study is done on performance management frameworks, process improvement methods, data visualization techniques, and dashboard solutions that are already out there. The Lean, Six Sigma, and balanced scorecard methods are looked at for success indicators and metrics. It also looks into data visualization tools' design principles and methods like dashboards, tables, charts, and interactive features. Interviews, observations, document reviews, and internal files are ways a case organization gathers both primary and secondary data. Key performance indicators for quality, delivery, productivity, safety, and customer happiness are found in several different business roles and processes. To look at performance trends and relationships, SPSS is used for descriptive statistics and hypothesis testing. Interactive dashboard examples that use different data visualization methods are created by thinking about how the user will experience it, how it will work with other systems, and how it will help them make decisions. Dashboards let you dig deeper into processes, determine why problems happen, and compare actuals to goals. Usability testing checks how easy the tools are and how well decision-makers can learn from them. The results show that dashboard analytics can make handling performance and improvement projects easier based on data. Insights made it possible to keep track of KPIs, find methods that were not working well, and work together across teams. There are also talks about problems with data quality and change management.

A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization

Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

Crop Monitoring and Smart Farming Through Mobile App

This study presents the development and implementation of a mobile application aimed at revolutionizing crop monitoring and smart farming practices. The application integrates advanced technologies such as remote sensing and the Internet of Things (IoT) and provides real-time data analysis and decision-making support for farmers. By leveraging the ubiquity and accessibility of mobile devices, this solution enables farmers to remotely monitor crop health, soil conditions, and environmental parameters. Through intuitive interfaces and data visualization tools, the application empowers farmers to make informed decisions, optimize resource utilization, and enhance productivity in agricultural operations. This research highlights the potential of mobile technology to drive innovation and sustainability in modern farming practices.

Bibliometric Analysis of Trends and Patterns in Sustainable and Responsible Investments (SRI) Research from 2000-2024

This study provides a comprehensive bibliometric analysis of Sustainable and Responsible Investments (SRI) research from 2000 to 2024, utilizing data visualization tools to map the evolution and current state of the field. Through thematic clusterization, temporal trends, author collaboration networks, and potential research topics, the research offers insights into the dynamic nature of SRI. Initial findings show a progression from foundational concepts to more nuanced discussions around impact investing and sustainability indices. The analysis of author collaboration networks highlights key influencers and suggests potential areas for future collaboration. Additionally, the identification of underexplored areas such as ethical investment frameworks and the impact of social investments indicates new directions for research. This paper underscores the importance of SRI in aligning financial investments with global sustainability goals, providing a roadmap for future research and practice in the field.

Study the Level of Inclusion in Disaster Risk Reduction for Inclusive Elementary School in Indonesia

This research aims to explain how disaster risk reduction in elementary schools can be implemented inclusively and to comprehensively understand research trends regarding DRR in inclusive elementary schools in Indonesia in 2013-2023.The research was conducted using bibliometric methods based on the 2013-2023 Google Scholar database. Data collection is supported using the Publish or Perish (PoP) application. We also use VOSviewer as a data visualization tool capable of creating meshes from processed data sets.From this search, 999 articles were found with the keywords inclusive, disaster risk reduction, elementary schools, and Indonesia. Found 11 terms based on network visualization which were grouped into 2 clusters. The term inclusive school appears 4 times and the term disaster risk reduction appears 14 times. Based on the research results, efforts are needed to be made through inclusive education programs in elementary schools that are in accordance with the need to reduce disaster risk in schools both before, during and after disasters

The Food-Energy-Water Nexus: Assessing undergraduate students’ decision making about complex socio-hydrologic issues supported by Hydroviz

The Food-Energy-Water (FEW) Nexus envisions interactions between water, energy, and food systems, in which water allocation decisions constitute complex socio-hydrologic issues. Undergraduate students need to develop skills to understand and make effective decisions about the FEW Nexus. We implemented a comprehensive curriculum where undergraduate students engaged in a systematic decision-making process around a FEW Nexus challenge after collecting and processing information from an online data visualization tool—Hydroviz. We ask (a) what components of the decision-making process were students able to engage in most effectively, and (b) what factors (e.g., students’ demographics, prior knowledge, use of Hydroviz, and curricular resources, among others) support students’ overall decision-making outcomes? The study is based on a mixed-methods approach in which we analyzed n = 94 students’ responses to a decision-making scenario, and n = 13 semistructured interviews from the second iteration of the module in 2021. Results suggest that students may need additional support to better frame the decision-making problem to provide a more in-depth and localized description, including their use of collected evidence to support their description. Factors that supported students’ decision-making outcomes include (a) their personal values and experiences and (b) scaffolding elements of the module, in which they were able to decompose the elements of the problem, prioritize different values in their analysis, and question their initial approaches to further analyze their choices. More information about the energy sources, and the impacts on the community and environment, would be beneficial. No differences between demographic groups were observed. The study can provide insights to enhance instructors’ use of the curricular resources and curricular design, thereby supporting undergraduate instruction to foster students’ engagement in decision making about socio-hydrologic issues.

Exploring the Frontier of Data Science: Innovations, Challenges, and Future Directions

Data science, an interdisciplinary field, has profoundly transformed our understanding and utilization of data across diverse sectors such as healthcare, finance, marketing, and transportation. With the rapid advancements in computational power and the exponential growth of data from digital sources, sophisticated methodologies and tools have emerged, enabling deeper insights and more informed decision-making. This paper explores the latest innovations in data science, focusing on advancements in machine learning algorithms, big data technologies, and data visualization tools. It highlights the development of cutting-edge techniques that enhance predictive accuracy, optimize resource allocation, and improve operational efficiencies. Additionally, we address the key challenges faced by practitioners, including ensuring data quality and management, navigating ethical and privacy concerns, and bridging the skill gap within the workforce. By examining these aspects, the paper provides a comprehensive overview of the current state of data science and its implications for future research and application. The insights gathered aim to guide researchers and professionals in leveraging data science advancements while addressing the inherent challenges to maximize the potential benefits across various industries.

Novel approaches of borehole‐GPR data processing and visualization – application for unexploded ordnance detection

AbstractBorehole ground‐penetrating radar (BGPR) measurements allow for the detection of objects and structures in the subsurface and are often applied to the detection of unexploded ordnance (UXO). If omnidirectional borehole antennas in reflection mode are used for the measurement, the localization of UXO is only possible if the data from a multitude of boreholes are analysed. Data analysis is usually still done by manual picking of reflections. We propose novel approaches to process and visualize data from BGPR measurements in a more advanced and appealing manner. Therein, the reflected energy recorded in the radargrams is projected back to all potential reflection points in the three‐dimensional space around the boreholes. If the projection direction is considered, we obtain a vectorized energy projection image. Superposition of projected energy yields an easy‐to‐grasp indicator of possible locations of UXO and of regions of interest that ought to be investigated in more detail. These approaches have been applied to synthetic data and to data measured on a test site with buried UXO. The results show that energy projection is a useful tool for BGPR data visualization, although the result is dependent on data pre‐processing. The proposed methods provide novel representations of BGPR data based on an objective algorithm which will at least complement the conventional methods.

Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification

Protein-protein and protein-water hydrogen bonding interactions play essential roles in the way a protein passes through the transition state during folding or unfolding, but the large number of these interactions in molecular dynamics (MD) simulations makes them difficult to analyze. Here, we introduce a state space representation and associated "rarity" measure to identify and quantify transition state passage (transit) events. Applying this representation to a long MD simulation trajectory that captured multiple folding and unfolding events of the GTT WW domain, a small protein often used as a model for the folding process, we identified three transition categories: Highway (faster), Meander (slower), and Ambiguous (intermediate). We developed data sonification and visualization tools to analyze hydrogen bond dynamics before, during, and after these transition events. By means of these tools, we were able to identify characteristic hydrogen bonding patterns associated with "Highway" versus "Meander" versus "Ambiguous" transitions and to design algorithms that can identify these same folding pathways and critical protein-water interactions directly from the data. Highly cooperative hydrogen bonding can either slow down or speed up transit. Furthermore, an analysis of protein-water hydrogen bond dynamics at the surface of WW domain shows an increase in hydrogen bond lifetime from folded to unfolded conformations with Ambiguous transitions as an outlier. In summary, hydrogen bond dynamics provide a direct window into the heterogeneity of transits, which can vary widely in duration (by a factor of 10) due to a complex energy landscape.

AI-Enhanced Data Visualization: Transforming Complex Data into Actionable Insights

Purpose: The purpose of this study is to explore how artificial intelligence (AI) becomes a part of data visualization. Thus, data from complex datasets are transformed into dynamic, interactive, and personalized visual experiences that will help in deeper insights and actionable knowledge. The research is supposed to design a holistic system and rules for using AI to make data visualization more effective and super interactive for the users. Methodology: The methodology involves the in-depth examination of artificial intelligence-based data visualization tools and platforms by using case studies. The study analyses the impact of AI technologies such as machine learning, natural language processing, and augmented and virtual reality on the scalability, interactivity, and personalization of data visualizations. The sentence also talks about the analysis of the moral factors that are part of the process of introducing AI in data visualization. Findings: The findings indicate that AI greatly improves the process and the quality of data visualization, thus, it makes possible the management of big, complicated, multi-dimensional datasets in a more efficient and precise way. The AI-driven tools give the users the opportunity to see the actions that are happening in real-time, predict the results, and personalize the tools according to their individual needs, thereby increasing the decision-making processes. Furthermore, ethical issues like data privacy, bias, and transparency must be well managed. This research has the distinctive feature of providing a theoretical framework that emphasizes the importance of AI in the development of data visualization technologies. Unique contribution to theory, policy and practice: In practice, it gives the rules for the implementation of AI tools to achieve more effective and user-focused visualizations. The policy focuses on the necessity of ethical standards in AI deployments, which means the data visualization practices should be transparent, accountable, and bias-free, thus creating trust and reliability in the AI applications.

Analysis of Wine Using Python Graphic Walker

The exploration and analysis of wine characteristics have long been a fascination for connoisseurs and researchers alike. This abstract delves into the innovative use of Python Graphic Walker in dissecting the multifaceted attributes of wine. Leveraging the power of computational techniques, this study aims to provide a comprehensive understanding of various components contributing to wine quality, such as acidity, sweetness, aroma, and flavor profile. Python Graphic Walker, a versatile tool for data visualization and analysis, offers a dynamic framework for examining intricate patterns within wine datasets. Through its adaptable nature and rich library support, the Graphic Walker facilitates the creation of insightful visualizations, enabling researchers to discern hidden correlations and trends in wine-related data.

INTEGRATİON OF ARTİFİCİAL INTELLİGENCE (AI) İN TASK MANAGEMENT SYSTEMS

The incorporation of artificial intelligence (AI) into task management systems has become a disruptive force in today's fast-paced work environments, changing how teams plan, prioritize, and complete tasks. This trend is best illustrated by platforms such as Trello, Monday.com, and Asana, which use AI technology to streamline project workflows, automate tedious chores, and offer insightful recommendations. These systems enable users to improve decision-making, expedite procedures, and increase productivity using AI-driven capabilities like predictive analytics, automated task assignment, and smart suggestions. Through the utilization of natural language processing (NLP) and data visualization tools, these systems facilitate data-driven insights, preemptive risk management, and intuitive interaction. The incorporation of AI in task management signifies a paradigm change towards more intelligent, cooperative work practices, as companies depend more and more on AI to enhance human talents and boost productivity. This article addresses the consequences of this technological advancement for the nature of work in the future, presents real-world examples of AI-powered features in popular platforms, and examines the many advantages of integrating AI into task management. Keywords: task management, artificial intelligence, task management systems, integration of AI, automated task assignment, Natural Language Processing (NLP).

Current state of the art and future directions: Augmented reality data visualization to support decision-making

Augmented Reality (AR), as a novel data visualization tool, is advantageous in revealing spatial data patterns and data-context associations. Accordingly, recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and effectiveness. As a result, AR has been applied in various decision support systems to enhance knowledge conveying and comprehension, in which the different data-reality associations have been constructed to aid decision-making.However, how these AR visualization strategies can enhance different decision support datasets has not been reviewed thoroughly. Especially given the rise of big data in the modern world, this support is critical to decision-making in the coming years. Using AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant data. Integrating the decision-maker’s contexts and the DSS support in AR is a difficult challenge. This paper outlines the current state of the art through a literature review in allowing AR data visualization to support decision-making.To facilitate the publication classification and analysis, the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical context. Based on this taxonomy and a decision support system taxonomy, 37 publications have been classified and analyzed from multiple aspects. One of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support systems. Along with this novel tool, the paper discusses the current state of the art in this field and indicates possible future challenges and directions that AR data visualization will bring to support decision-making.

The Role of Data Visualization in Decision Making – Case of D-mart

Data visualization is crucial for informed decision-making in today's data-driven world. It transforms complex data sets into visually engaging representations, enhancing cognitive processes and fostering insights. The report explores the principles, tools, and impact of data visualization, highlighting its ability to streamline operations, identify market trends, optimize resource allocation, and mitigate risks. It provides an overview of various data visualization tools and types, ranging from basic charts and graphs to advanced interactive dashboards and geospatial visualizations. Organizations can leverage these tools to gain a competitive edge and make informed decisions that drive sustainable success. By harnessing the power of visual analytics, decision-makers can make informed choices that drive business growth and innovation.

Pagadian Diocesan Schools Information Monitoring System through Data Visualization

The Pagadian Diocesan Schools Information Monitoring System through Data Visualization is a web-based application meticulously developed to address the challenges of managing educational data across multiple schools within the Pagadian Diocese. Utilizing HTML, MySQL, PHP, CSS, and Bootstrap, the system streamlines the login process for administrators, enhances data monitoring, and provides a centralized view for superintendents. Through advanced data visualization tools, the platform presents essential metrics such as student population, income, and expenses, contributing significantly to the Pagadian government’s education initiatives while adhering to ethical data usage practices. Continuous user feedback ensures the refinement of the system, guaranteeing efficient and effective data management within the Pagadian Diocese. The study also encompasses the design, development, and testing phases of the system, following the Waterfall Model. Through rigorous testing, the system demonstrated commendable performance, stability, and security measures, with high user-friendliness and compatibility. The ISO/Logical Summary Report identified strengths in Functional Stability, Portability, Reliability, Security, Usability, and Maintainability, leading to a Total ISO/Logical Flow score of 90.94%. Overall, the system promises a smooth, effective, and dependable user experience, with room for improvement in UI design and error handling to enhance its performance further.

Behavioural determinants of the healthcare providers in Ukraine: Focus on competition context

The development of the healthcare system is an important task for every country, given that long-term economic and social development is absolutely impossible without a healthy nation. Therefore, improving public health and ensuring that the maximum number of people have access to quality healthcare services is one of the state's strategic priorities. In this regard, the authors of the paper set a goal to establish how the reform of the healthcare system in Ukraine has led to changes in the behavioural determinants of healthcare providers and recipients in the domestic healthcare market. The paper uses such research methods as comparison, visualization, graphical data visualization tools, and statistical methods of data analysis. The study revealed that Ukrainian citizens got a chance to freely choose their physicians by signing declarations with them, which led to increased competition among physicians and other doctors and healthcare facilities; at the same time, the number of declarations signed with private healthcare facilities increased significantly. Furthermore, the developed econometric model allows us to identify statistically significant relationships between the amount of state funding and lifestyle and environmental indicators, which will allow us to improve the mechanisms and amount of funding in the sector, improving the health of the nation in the new competitive environment.

Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making

Background Antimicrobial Resistance (AMR) is a critical global public health concern, demanding effective tools for research, data analysis, and decision-making. This study proposes a groundbreaking approach to empower the global AMR research community by introducing interactive Geographic Information System (GIS) dashboards. These dashboards aim to facilitate comprehensive data analysis of AMR across multiple countries, providing insights into antimicrobial usage (AMU), resistance patterns, and geographic distribution. Methods The novel approach involves the development of GIS dashboards that integrate and harmonize data from diverse sources, including clinical laboratories, surveillance networks, and public health agencies. Objective 1 focuses on creating a dashboard encompassing all countries, offering comprehensive data analysis capabilities and visualization tools. Objective 2 entails building a focused dashboard specifically for Kenya and Uganda, allowing for comparative analysis of AMR in these regions. Objective 3 involves the generation of a simulated dataset for Kampala, Uganda, addressing data limitations in that specific area. Results The GIS dashboards serve as powerful tools for visualizing and analyzing AMR-related datasets, providing stakeholders with a comprehensive view of the global AMR landscape. These dashboards offer valuable insights into antimicrobial usage, resistance patterns, and geographical distribution. The centralized platform facilitates data exploration and analysis, aiding researchers, policymakers, and healthcare professionals in making informed decisions to combat AMR. Conclusions In conclusion, the study demonstrates that the developed GIS dashboards empower stakeholders by providing valuable insights and informed decision-making capabilities. The dashboards serve as essential tools for addressing the global challenge of AMR, allowing for a deeper understanding of the problem and informing effective strategies. The approach outlined in this study has the potential to significantly contribute to the ongoing efforts to combat AMR on a global scale.