Data Visualization Approach Research Articles

An Enhanced Affective Computing Technology for Fostering an Emotionally Healthy Workplace

The mental health of workers is of paramount importance in today’s fast-paced and demanding workplaces. This research establishes a strong connection between affective decision-making and mental health, presenting a novel method to improve affective computing technologies for developing emotionally healthy workplaces. We use Multinomial Naive Bayes Integrated Gated Recurrent Units (MNB-GRU) for classification and prediction to reach our goals. This dataset includes many measures of mental health, working climate, and individual variables. Data Exploration is performed to learn more about the properties of the dataset, and Preprocessing Grouping is used to get the data ready for analysis. Relationships between emotional decisionmaking and mental health markers are shown using data visualization approaches to give intuitive insights. To evaluate the reliability of the connection, a correlation analysis is used in Model Assessment. Understanding how people are feeling emotionally at work can be gained via this assessment, which examines the degree of association between affective decision-making and mental health. As a result of using categorization methods to divide the workforce into several categories based on their emotional well-being, we can better assist businesses in meeting the varying demands of their staff members. This method guarantees that efforts to improve mental health are focused and productive. By creating a solid connection between effective decisionmaking and mental health, businesses can take preventative measures to aid their workers' emotional well-being and create a more upbeat and productive workplace.

Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

Visual analysis of oceanic data for marine ecosystems

We present a data visualization approach that allows users to interactively analyse sequential movement patterns and automatically extract rules for marine animal behaviour. This enhances the ability of oceanic experts to understand marine ecosystems. Traditional time-series analysis, basic statistical methods, and single-view visualizations often fail to capture the intricate spatial and temporal dynamics of marine ecosystems. Our visual analytics approach, that leverages sequential pattern mining and rule mining techniques, integrates SPMF's Vertical Mining of Maximal Sequential Patterns (VMSP) algorithm and T-Rule Growth algorithm to efficiently extract and visualize movement patterns and behavioural rules of marine animals. The system also incorporates automated pre-processing to clean and prepare large datasets, reducing the time and effort required for manual handling. The visualization presents oceanic data from detection stations, allowing researchers to discover patterns in the movements of marine species. Our interface also provides geographical visualization of selected patterns or rules and helps users to navigate and find the relevant information. Key principles of our model include multi-view visualizations that provide comprehensive insights by displaying data from various perspectives simultaneously, and geo-spatial time-varying data visualization that captures and represents the dynamic movements of marine species. This approach allows domain experts to interactively explore and analyse sequential patterns and rules, enhancing their ability to make informed decisions. Additionally, feedback received, and insights gained from specialized professionals was used to determine if this approach would be a beneficial complement to existing practice.

A Preliminary Data Visualization Approach to Vestibulocochlear Diseases Based on Multiple Scalar Dimensions.

For vestibulocochlear diseases, traditional clinical history-taking, and the terminology of widely taught nosologic taxonomy, have misleading implications that can lead to errors in diagnosis and therefore in treatment. In the interest of facilitating differential diagnosis while simultaneously recognizing that many of these diseases may not be as discrete as textbooks suggest, we propose a data visualization approach focusing on several continuous scalar dimensions in the domains of anatomy, physiology, and chronology. We illustrate the application of this approach to several categories of clinical presentation.

Junior High School Student Models in Online Learning as A Post-Covid Effect

Junior High School Student Models in Online Learning as A Post-Covid Effect

Architectural Visualization Approach Using Google SketchUp and Lumion on the Development of Maritime Tourism

This study aims to portray the strategy and design of marine tourism development through a data visualization approach using Google SketchUp and Lumion. The type of research conducted is Research and Development (R&D), employing the ADDIE approach (Analysis, Design, Development, Implementation, and Evaluation). The findings of this study reveal that the miniature design of Pulau Sembilan on Larea-rea Island incorporates local cultural themes in the construction of Rulan (Nine House), with a visualization of the roof following the design of the traditional Karampuang house. Additionally, the researcher presents designs concerning the jetties of Larea-rea Island and the product facility designs. Ultimately, contributes to development of Rulan (Nine House) and the efficient land utilization contributes to the region's income enhancement in the tourism sector of Larea-rea Island. It is recommended that future research on marine tourism development should involve a more comprehensive analytical framework that considers social, economic, and environmental aspects. This will help in understanding the overall impacts of marine tourism development. It is also important to investigate the ecological impacts of tourism and come up with effective mitigation strategies to prevent harm to the marine environment. Another interesting research area is to integrate the perspectives of local communities and their participation in the planning and implementation of tourism development. This will ensure the sustainability of such projects and gain community acceptance.

Predictive Modeling of Seismic Events: A Comparative Analysis Of K-Nearest Neighbors and Random Forest Algorithms

This study processes and predicts seismic data using data visualization approaches, K-nearest neighbors (KNN) and the random forest (RF) algorithms. The analysis's dataset includes a number of variables connected to earthquakes. The primary goal is to devise a forecast algorithm capable of accurately categorizing seismic events, data visualization tools are utilized to gain insights into the dataset, producing informative charts that depict the distribution and correlations among different variables. This visual evaluation aids in pinpointing anomalies or trends, facilitating a deeper understanding of the data's characteristics and guiding decisions during the modeling phase. Subsequently, the KNN method classifies earthquake occurrences based on their attributes, predicting the class label by considering the characteristics of its nearest neighbors. Additionally, Accurate classification of seismic events is enhanced by using RF, an ensemble learning technique that combines many decision trees to produce predictions. To optimize outcomes, the study adjusts the random forest model's hyperparameters through cross-validation. The study compares the performance of KNN and RF using a confusion matrix. The confusion matrix shows a thorough insight of categorization performance, which provides a comprehensive view of categorization efficacy. This assessment underscores the models' precision and effectiveness in classifying seismic events.

SAP: Fiori Application and its Integration with AI

Abstract: Bridging the Gap Between Data and Decision: SAP Fiori's AI Integration Revolutionizes Business Processes. SAP Fiori, the sleek user interface for SAP solutions, is undergoing a metamorphosis, empowered by the transformative potential of Artificial Intelligence (AI). This integration transcends the traditional data visualization approach, weaving intelligence directly into the user experience. Imagine a purchase order form intelligently suggesting optimal suppliers based on past performance and market fluctuations, or a sales dashboard predicting customer churn with uncanny accuracy. This is the future of SAP Fiori, where AI acts as a co-pilot, guiding users through complex tasks, surfacing relevant insights, and automating repetitive processes. By seamlessly integrating AI, SAP Fiori empowers users to make informed decisions faster, optimize workflows, and unlock new levels of business agility. These abstract paints a picture of the exciting possibilities that emerge when SAP Fiori and AI join forces, transforming the way businesses operates. S/4HANA Cloud migration and AI integration is a powerful combination that can help businesses of all sizes to achieve their transformation goals and stay ahead of the competition. By migrating to S/4HANA Cloud and integrating AI features, businesses can improve their operational efficiency, increase their agility, accelerate their innovation, and reduce their costs. In this paper, author has explained the requirement of SAP S/4HANA Cloud RISE Enterprise Solution Automation with Artificial Intelligence features – a current business transformation needs to achieve business goal.

Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented Reality

Embedding data into the physical environment using augmented reality (AR) is a practical approach for data visualization as it offers a large and flexible display space on or around the physical referent, i.e. the physical object to which the data is related. Yet current interaction in such context is often performed using cumbersome dedicated devices, tiring mid-air gestures or awkward on-body input. In this paper, we investigate the use of the physical referent itself as a support for input interaction with an embedded space-time cube (STC) representation. Hence, we first identify the most promising mappings between the physical features of the referent (edges, faces, corners) and the STC dimensions. Then, we design three data selection techniques using the physical referent and compare them to mid-air gestures when performing selection tasks on the STC. Overall, our work demonstrates that using the physical referent to support input interaction with embedded data representations is an efficient and comfortable approach for data selection in standing and sitting situations.

Exploring person-specific associations of situational motivation and readiness with leisure-time physical activity effort and experience.

Identifying determinants of leisure-time physical activity (LTPA) often relies on population-level (nomothetic) averages, potentially overlooking person-specific (idiographic) associations. This study uses an idiographic perspective to explore how subjective readiness and motives for LTPA relate to volitional effort (duration, intensity) and affective experience (pleasure, displeasure). We also highlight the potential for different interpretations when data are averaged within individuals and assessed using a variable-centered approach. Participants (N = 22, 25±8 years old, 54.5% women) were asked to continue their regular PA patterns for 10 weeks. Ecological momentary assessment procedures allowed participants to provide pre-activity reports (physical, cognitive, emotional readiness and situational motive for activity) and post-activity reports (activity type, duration, perceived exertion, ratings of affective valence). Spearman rank correlation was implemented to interpret within- and between-person associations. Data visualization approaches were used to showcase person-specific differences in associations. Participants provided 519 reports of LTPA (24±11 events/person), which displayed between- and within-person variety in type, duration, intensity, and affective experience. Exemplar cases highlight discrepancies in interpretation based on level of analysis, such that the nomothetic association (rho = .42, p = .05; 95% CI -.02, .72) between motive to replenish energy and LTPA duration was observed in only one within-person analysis (41% were weak-to-large inverse effects). Alternatively, the negligible nomothetic association (rho = .02, p = .93; 95% CI -.41, .44) between physical readiness and LTPA-related affect did not reflect the 59% of within-person analyses showing moderate-to-large, positive effects. Future research aiming to identify determinants of LTPA effort and experience should integrate contemporary, idiographic analyses in early-stage research for developing person-specific strategies for LTPA promotion.

Data visualization approach for business strategy recommendation using power BI dashboard

Data visualization approach for business strategy recommendation using power BI dashboard

A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status

This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used "Qgis2web" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.

Examining students’ course trajectories using data mining and visualization approaches

The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.

Pattern recognition of hematological profiles of tumors of the digestive tract: an exploratory study.

In this study, we aimed to apply laboratory blood analysis to identify the hematological (based on hemoglobin concentration, erythrocytes, hematocrit, and RDW count) profiles associated with the most prevalent forms of digestive tract malignancies. Furthermore, we aimed to evaluate how these profiles contributed to distinguishing these tumors at diagnosis. We collected data from the date of ICD-10 diagnostic coding for C15 esophagus, C16 stomach, C18 colon, and C19 rectum tumors of 184 individuals. The statistical analysis and data visualization approaches, notably the heat map and principal component analysis (PCA), allowed for creating a summary hematological profile and identifying the most associated parameters for each pathologic state. Univariate and multivariate data modeling and ROC analysis were performed in both SPSS and Python. Our data reveal unique patterns based on tumor development anatomical location, clustering the C18 colon and C19 rectum from the C15 esophagus and C16 stomach. We found a significant difference between C16 stomach carcinoma and the other tumors, which substantially correlated with raised RDW in conjunction with low hemoglobin concentration, erythrocytes, and hematocrit counts. In contrast, C18 colon carcinoma had the higher red blood cell count, allowing for the best classification metrics in the test set of the binary logistic regression (LR) model, accounting for an AUC of 0.77 with 94% sensitivity and 52% specificity. This study emphasizes the significance of adding hematological patterns in diagnosing these malignancies, which could path further investigations regarding profiling and monitoring at the point of care.

Building Information Modeling and Building Performance Simulation-Based Decision Support Systems for Improved Built Heritage Operation

Adapting outdated building stocks’ operations to meet current environmental and economic demands poses significant challenges that, to be faced, require a shift toward digitalization in the architecture, engineering, construction, and operation sectors. Digital tools capable of acquiring, structuring, sharing, processing, and visualizing built assets’ data in the form of knowledge need to be conceptualized and developed to inform asset managers in decision-making and strategic planning. This paper explores how building information modeling and building performance simulation technologies can be integrated into digital decision support systems (DSS) to make building data accessible and usable by non-digital expert operators through user-friendly services. The method followed to develop the digital DSS is illustrated and then demonstrated with a simulation-based application conducted on the heritage case study of the Faculty of Engineering in Bologna, Italy. The analysis allows insights into the building’s energy performance at the space and hour scale and explores its relationship with the planned occupancy through a data visualization approach. In addition, the conceptualization of the DSS within a digital twin vision lays the foundations for future extensions to other technologies and data, including, for example, live sensor measurements, occupant feedback, and forecasting algorithms.

Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19.

The COVID-19 pandemic is the first major pandemic of the digital age and has been characterised by unprecedented public consumption of spatial and temporal disease data, which can enable greater transparency and accountability of governments to the public for their public health decisions. A variety of state and non-state actors have collated and presented maps, charts, and plots of data related to the pandemic in both static and dynamic formats. In particular, there has been a proliferation of online dashboards presenting data related to the pandemic. The sources and types of information displayed have evolved rapidly during the pandemic, with a general trend towards providing more specialised information pertinent to specific aspects of epidemiology or disease control, as opposed simply to disease and death notifications. Limited evaluation of the quality of COVID-19 data visualisation tools has been conducted and significant effort now needs to be spent on standardisation and quality improvement of national and international data visualisation systems including developing common indicators, data quality assurance mechanisms and visualisation approaches, and building compatible electronic systems for data collection and sharing. The increasing availability of disease data for public consumption presents challenges and opportunities for government, media organisations, academic research institutions, and the general public. A key challenge is ensuring consistency and effectiveness of public health messaging to ensure a coordinated response and public trust in intervention strategies. Capitalising on opportunities for greater government accountability for public health decision-making, and more effective mobilisation of public health interventions, is predicated on the provision of accurate and timely information.

Predicting olivine formation environments using machine learning and implications for magmatic sulfide prospecting

Abstract Global volcanic and plutonic olivines record the compositional characteristics and physicochemical conditions of the parental magmas. Thus, they have significant potential for use as petrogenetic discriminators of the olivine formation environment and prospecting indicators for potential host rocks of magmatic sulfide deposits. Several data visualization approaches have been proposed by researchers to determine olivine origins. However, they can only discriminate specific olivine populations and require the incorporation of trace elements for which data are lacking globally. In this study, a machine-learning method consisting of the random forest algorithm and the synthetic minority oversampling technique (SMOTE) is used to discriminate the crystallization environments of olivine and predict the sulfide potential of olivine-bearing mafic-ultramafic intrusions. We employ a global data set of 24 341 olivine samples from 12 environments to determine the contents of MgO, FeO, Ni, Ca, Mn, and Cr and the Fo number [100 × Mg/(Mg+Fe)]. The results indicate that the proposed method can classify olivine into genetically distinct populations and distinguish olivine derived from mineralized intrusions from that derived from sulfide-barren intrusions with high accuracies (higher than 99% on average). We develop a dimensionality reduction algorithm to visualize the olivine classifications using low-dimensional vectors and an olivine classifier (accessible at http://101.33.204.62:8080/olivine_web/main.html, China University of Geosciences, Beijing). The model is used successfully to identify the contributions of distinct sources to regional magmatism using olivines from the late Permian picrite and basalt along the western margin of the Yangtze block (SW China) and to predict the sulfide potential of the newly discovered Qixin mafic-ultramafic complex in the southern Central Asian Orogenic Belt (NW China). The findings suggest that the proposed approach enables the accurate identification of olivine origins in different formation environments and is a reliable indicator suitable for global Ni-Cu-platinum group element (PGE) exploration.

Cold supply chain of leafy green vegetables: a social network analysis approach

PurposeCold supply chain technology is critical for extending the shelf life of perishable leafy green vegetables. This study aims to investigate the concept of managing leafy green products using cold supply chain technology and visualise the findings.Design/methodology/approachUsing expert interviews and data visualisation approaches, this study examines how organisations deal with the complexity of cold supply chain processes and networks. Thematic data analysis was conducted. Two types of software were used to accomplish the research objectives. The first software used AntConc version 3.5.8 with word frequency (N-gram) analysis, whereas the second software, VOSViewer offered co-occurrence network visualisation and cluster analysis.FindingsThe findings show that the appropriate design of cold chain technology is critical in ensuring the freshness and quality of leafy green vegetables. The primary goal of managing the complexity of the cold supply chain is to achieve product freshness and energy efficiency. Regardless of the importance of energy efficiency, cold supply chains require warehouse management solutions for transportation and storage.Practical implicationsThis study found that proper design and selection of appropriate technology in the cold supply chain have driven the companies to improve the firms’ competitive advantage while delivering the best quality of perishable leafy green food products. In addition, the freshness, quality, safety, and health of leafy green vegetables will be determined by the company’s capacity to handle long-distance transportation and select the appropriate distribution channels and storage. Warehouse management system technology was found to be secondary compared to cold chain technology, although distribution and warehousing practices are critical for supply chain performance.Originality/valueThis study has established the conceptual indicators based on best practices and outcomes for the cold supply chain. This study argued that cold supply chain management and performance should be monitored independently. Furthermore, the theory of technological adoption can be expanded to include product nature as a driver. Finally, this study has established cold chain best practices based on a perishable supply chain perspective. The findings of this study can promote healthy foods to solve zero hunger and achieve sustainable development goals. Although this study demonstrates that technology improves supply chain practises, cold storage and logistics benefit the most from technological advancements. In contrast, non-cold supply chains benefit from technology-driven improvements in performance.

A spatial data integration and visualization approach for occupational health and safety risks management: Application to Algerian electricity and gas company

AbstractIn the safety 4.0 era, occupational health and safety (OHS) related data has become one of the most valuable assets for organizations. Risk Management in the OHS field is a data‐driven process. However, geospatial data has not been fully considered and few works have used visualization for decision‐making purposes. Our research was conducted using a design science research methodology. It is a methodology for creating and evaluating IT artifacts to solve identified organizational problems. We used Wang's safety intelligence framework and the recommendations of the ISO 45001 model from extant literature to inform the Design. While creating our artifact, we chose to make an inventory of occupational risk prevention practices in Algeria. A qualitative study using interviews with experts in the field consolidated the requirements for such a system. The key findings from the analysis of collected data state that the development of safety data‐driven decision‐making requires top management support and involvement, which is in line with previous studies. A second concern is the availability of spatial data. The designed artifact is an approach called spatial data integration and visualization approach for occupational health and safety management systems (SDIVA‐OHSMS). SDIVA‐OHSMS provides a practical approach for organizations in charge of OHS services to implement OHS data‐driven decision‐making solutions highlighting spatial and semantic dimensions. The evaluation was performed through a representative case study within the Algerian electricity and gas company (Sonelgaz), employing expert opinion and software implementation. As a result, experts concluded that such systems could provide a more comprehensive and accurate view of occupational health issues and guide further prevention policies.

Using Text Mining and Data Visualization Approaches for Investigating Mental Illness from the Perspective of Traditional Chinese Medicine

Background and Objectives. Anxiety and depressive disorders are the most prevalent mental disorders, and due to the COVID-19 pandemic, more people are suffering from anxiety and depressive disorders, and a considerable fraction of COVID-19 survivors have a variety of persistent neuropsychiatric problems after the initial infection. Traditional Chinese Medicine (TCM) offers a different perspective on mental disorders from Western biomedicine. Effective management of mental disorders has become an increasing concern in recent decades due to the high social and economic costs involved. This study attempts to express and ontologize the relationships between different mental disorders and physical organs from the perspective of TCM, so as to bridge the gap between the unique terminology used in TCM and a medical professional. Materials and Methods. Natural language processing (NLP) is introduced to quantify the importance of different mental disorder descriptions relative to the five depots and two palaces, stomach and gallbladder, through the classical medical text Huangdi Neijing and construct a mental disorder ontology based on the TCM classic text. Results. The results demonstrate that our proposed framework integrates NLP and data visualization, enabling clinicians to gain insights into mental health, in addition to biomedicine. According to the results of the relationship analysis of mental disorders, depots, palaces, and symptoms, the organ/depot most related to mental disorders is the heart, and the two most important emotion factors associated with mental disorders are anger and worry & think. The mental disorders described in TCM are related to more than one organ (depot/palace). Conclusion. This study complements recent research delving into co-relations or interactions between mental status and other organs and systems.