Prerpints.org logo

You are currently viewing a beta version of our website. If you spot anything unusual, kindly let us know.

Leave Feedback
Preprint
Article

HCI and Data: Interacting in a New Era of Virtualization

Altmetrics

Downloads

168

Views

73

Comments

0

This version is not peer-reviewed

Submitted:

28 June 2024

Posted:

01 July 2024

You are already at the latest version

Alerts
Abstract
The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.
Keywords: 
Subject: Computer Science and Mathematics  -   Information Systems

1. Introduction

In the digital era, the convergence of Human-Data Interaction (HDI) and Data Virtualization (DV) stands as a cornerstone for enhancing our ability to access, understand, and ethically manage data [1]. This paper explores the synergistic potential of these technologies to transform data-driven decision-making across various sectors.
In today’s data-driven world, the interaction between humans and data has become increasingly crucial, especially with the rise of innovative technologies like DV. This emerging field, which facilitates the integration and accessibility of data from diverse sources, has the potential to revolutionize how individuals and organizations interact with and utilize information [2,3].
HDI, as articulated by [4], centers on three fundamental aspects: data legibility, agency, and negotiability. These principles underscore the importance of enhancing data accessibility, empowering users to control their data, and fostering transparent interactions between data providers and users. Concurrently, DV provides a dynamic and flexible approach to integrating diverse data sources, representing a significant departure from conventional data management practices. [5]
The challenges posed by unstructured data from social networks, texts, images, and videos are profound, as these data forms resist simple analysis and interpretation [6]. Moreover, ethical considerations such as data ownership, consent, and the transparency of data use are increasingly critical. [7,8,9]. These issues are compounded by a general lack of data literacy, which can hinder the effective use and understanding of data and visualizations [10].
This article leverages contemporary case studies to demonstrate how DV can streamline access to diverse data sources and enhance the comprehension and usability of data. This, in turn, facilitates more informed and equitable decision-making processes. Through this exploration, the study contributes to the expanding academic discourse on HDI and outlines potential future research directions and practical applications for these transformative technologies.
The paper first provides a thorough examination of the theoretical foundations underpinning the fields of HDI and DV. It then proposes an enhancement to the existing HDI framework by strategically integrating key principles and concepts from DV. The study proceeds to analyze several relevant use cases, illustrating the diverse applications and potential benefits of integrating HDI and DV across a range of domains, including education, healthcare, and retail. Additionally, the paper introduces the concept of developing a comprehensive framework to guide the seamless integration of these two transformative technologies, offering guidance and considerations for designers and developers. Lastly, the study identifies key research challenges and concludes by presenting forward-looking proposals for future research endeavors that could further advance the synergistic potential of HDI and DV.

2. Theoretical Foundations

This section examines the theoretical underpinnings that support the study of Human-Data Interaction (HDI) and Data Virtualization (DV), establishing a foundation to understand their significance and potential impact across a range of disciplines.
HDI is an interdisciplinary field that examines the design, analysis, and assessment of systems where individuals engage with data. Mortier’s research [4] highlights three central challenges in HDI: data legibility, user agency, and data negotiability. These challenges underline the significance of making data more accessible and comprehensible, empowering users to govern their own data, and cultivating a constructive dialogue between users and data providers.
DV , on the other hand, represents a paradigm shift in data management. It provides a flexible and dynamic approach to integrating diverse data sources, enabling seamless access and analysis of data without the need for physical data consolidation. As noted in [11,12], DV can address the limitations of traditional data management methods by enhancing data accessibility, reducing data silos, and improving operational efficiencies.
The synergistic potential of HDI and DV lies in their ability to address the pressing challenges posed by the exponential growth of data in the digital age [13]. By combining these two fields, organizations and individuals can leverage data more effectively, make more informed decisions, and foster greater transparency and ethical practices in data utilization.
The following sections will provide a detailed exploration of HDI and DV, examining the relationship between the two subjects and discussing how advancements in DV technology can enhance human-data interactions in various applications.

2.1. Human-Data Interaction

Human-Data Interaction (HDI) is an interdisciplinary field that examines the design, analysis, and assessment of systems where individuals engage with data [4]. This emerging area of study focuses on understanding how people interact with data, the challenges they face, and the design principles necessary to create more effective and empowering data-driven systems.
As articulated by Mortier et al. [4], the core challenges in HDI can be organized into three key themes: legibility, agency, and negotiability.
  • Legibility addresses the need to make data more accessible and comprehensible to users, overcoming barriers such as data complexity, opaque data sources, and the prevalence of unstructured data forms.
  • Agency emphasizes the importance of empowering users to control their data, including the ability to access, modify, and understand how their data is being used.
  • Negotiability underscores the importance of fostering a constructive dialogue between data providers and users, promoting transparency and fairness in the governance and use of data.
These principles emphasise the significance of improving data accessibility, empowering users to govern their data, and cultivating transparent interactions between data providers and users.
The field of HDI has garnered growing interest in recent years, driven by the exponential proliferation of data and the ubiquity of data-driven technologies, which have profound implications for individuals, organizations, and society at large. [14]
The core purpose of HDI is to design, investigate, and assess systems that enhance data interactions, rendering them more intuitive and impactful. The key aspects of HDI can be enumerated as follows: [15]
  • Understanding Data Interaction: Examining how people perceive, interpret, and utilise data in various contexts, including the cognitive and behavioural aspects of data engagement [4,16].
  • Effective Human-Data Interfaces: Developing innovative interfaces, visualizations, and interaction modalities that improve data accessibility, comprehension, and usability [17].
  • Designing for User Empowerment: Creating systems that empower users to actively participate in data-driven decision-making and governance, promoting transparency and user agency [16,18] .
  • Ethical Considerations: Addressing the ethical implications of data use, including issues of privacy, bias, and the societal impact of data-driven technologies [19].
  • Interdisciplinary Collaboration: Fostering collaboration between diverse disciplines, such as Human-Computer Interaction, Information Visualization, Data Science, and Social Sciences, to advance the study and practice of HDI [15].
  • Data Accessibility: HDI prioritises ease of access to data through user-friendly interfaces, supporting diverse user needs and contexts. This involves designing intuitive systems that enable users, regardless of their technical expertise or background, to readily engage with and explore data [15,20].
  • Data Literacy: Beyond mere accessibility, HDI emphasises the importance of cultivating data literacy, empowering users to interpret, analyse, and draw insights from data. [21]
By addressing these core elements, HDI seeks to develop data-driven systems that are more intuitive, engaging, and empowering for users, thereby fostering a more transparent, accountable, and data-literate societal landscape [4]. This interdisciplinary field centres on designing data systems from a human-focused perspective, with the aim of augmenting individuals’ capacity to interact with data in substantive and empowering manners [22].

2.2. Data Virtualization

Building upon the foundational principles of HDI, which emphasizes a human-centric approach to data systems, we now delve into the transformative capabilities of DV [23]. This section will explore how DV, as a powerful technological paradigm, can effectively address the challenges of data accessibility, integration, and management, thereby further enhancing the principles of HDI and empowering users in their interactions with data.
DV is an innovative approach to data management that enables seamless access and integration of disparate data sources without the need for physical data consolidation [24]. This is achieved by creating a logical data layer that abstracts and unifies the underlying data infrastructure, providing users with a unified view of data regardless of its physical storage location or format.
The key benefits of DV include: [11,24,25]
  • Improved data accessibility: DV breaks down data silos and provides users with a centralized, real-time view of data from multiple sources, empowering them to access and leverage data more effectively.
  • Enhanced data integration: DV enables the integration of data from heterogeneous sources, facilitating the synthesis of information and the creation of a comprehensive, unified data landscape.
  • Agile data management: DV allows for dynamic data integration, enabling organisations to rapidly respond to changing data requirements and user needs without the need for complex, time-consuming data migration or ETL processes.
  • Cost-effectiveness: DV reduces the need for physical data consolidation and maintenance, leading to significant cost savings and operational efficiency.
  • Increased data governance: DV provides a centralized layer of control and governance over data, allowing for improved data security, compliance, and quality management.
DV builds on the solutions from batch and real-time data integration and is central to solving the problems of big data integration. By leveraging DV, organizations can more readily adapt their systems, as new reports can be quickly developed and existing reports can be efficiently modified. [26]
Through the application of DV, the principles of HDI can be further strengthened, leading to enhanced data accessibility, transparency, and user empowerment [27].

3. Enhancing HDI from DV

DV can significantly enhance the principles of HDI by addressing key challenges and limitations inherent in traditional data management approaches [15]. Through the implementation of DV, organizations can provide users with a more unified and comprehensive view of data, breaking down the siloed and fragmented nature of information that often hinders effective HDI [11].
By presenting a logical, integrated data landscape, DV can improve data accessibility, allowing users to more easily discover, access, and interact with the information they need to make informed decisions [28].
The agility and flexibility inherent in DV also enable organizations to rapidly adapt to evolving user requirements and emerging data sources, ensuring that the HDI experience remains relevant and responsive to changing needs [25,29]. Moreover, DV can enhance data transparency, as users gain a clearer understanding of the origins, lineage, and quality of the data they are interacting with. This, in turn, fosters greater trust and empowerment, as users can make more informed judgments and decisions based on a deeper comprehension of the data [11,29].
Overall, the convergence of HDI and DV holds immense potential for transforming the way individuals and organizations engage with data, ultimately leading to more informed, insightful, and impactful decision-making [1,3,15,29].

4. Experimental Research Insights

Exploring the intersection of HDI and DV, this section delves into the findings of three experimental research studies across the domains of Education, Health, and Retail.

4.1. Health

The healthcare industry has also witnessed the transformative impact of DV [29]. A study conducted by a leading healthcare provider showcased how the implementation of a DV strategy has enhanced patient outcomes and care coordination. By unifying data from disparate sources, such as electronic health records, patient monitoring devices, and administrative systems, healthcare practitioners gained a holistic view of patient health, enabling more informed and personalized treatment decisions. [30]
Additionally, the DV platform facilitated the development of advanced analytics and predictive models, empowering clinicians to leverage a comprehensive view of patient data. This enabled them to more accurately identify high-risk patients, proactively manage chronic conditions by tailoring treatment plans, and optimize resource allocation by anticipating and addressing emerging healthcare needs. Ultimately, these capabilities led to improved patient satisfaction, better health outcomes for the broader population, and more efficient utilization of healthcare resources. [31,32]

4.2. Retail

The retail sector has also seen significant benefits from the adoption of DV [33]. A study conducted by a major retail conglomerate highlighted how DV has transformed their ability to deliver personalized customer experiences and enhance operational efficiency.
By unifying customer data from various touchpoints, such as in-store transactions, e-commerce platforms, and loyalty programs, the retailer gained a 360-degree view of their customers, enabling them to develop more targeted marketing campaigns, personalized product recommendations, and tailored promotional offerings.Furthermore, the DV platform allowed the retailer to rapidly integrate new data sources, such as social media and IoT-enabled devices, enabling them to stay agile and responsive to evolving customer preferences and market trends [34].
In conclusion, the integration of DV within the framework of HDI has the potential to unlock unprecedented opportunities for users to engage with data in more meaningful and empowered ways.

4.3. Education

In the educational domain, the implementation of DV has demonstrated tangible benefits [11] in improving student outcomes and enhancing the overall learning experience [35].
A study conducted by a leading educational institution found that the adoption of a DV platform enabled seamless integration of disparate data sources, including student information systems, learning management systems, and assessment data. This unified data landscape allowed educators to gain a comprehensive, real-time understanding of student performance, attendance patterns, and engagement levels, empowering them to make data-driven, targeted interventions to support student success [11,35,36].
Furthermore, the agility afforded by DV enabled the institution to rapidly develop and deploy new analytics and reporting tools, facilitating the continuous improvement of educational strategies and the adaptation to evolving student needs [37]. By leveraging the principles of HDI, students also gained increased access to their own performance data, fostering self-reflection, goal-setting, and personalized learning experiences. [25,29,38,39]

5. Empowering Real-World Applications: In-Depth Use Cases of DV in Healthcare, Finance, and Education

This section presents detailed case studies that illustrate the practical implications of DV on HDI across different contexts. The analysis of specific case studies demonstrates the significant impact of DV on HDI.
The advantages of integrating HDI and DV have been observed across various industries, each with unique challenges and opportunities.
This paper explores in-depth use cases of DV in three key sectors: healthcare, finance, and education. These real-world scenarios elucidate how DV can substantially enhance decision-making, operational efficiency, and personalized services within each domain.

5.1. Healthcare: Enhanced Patient Care through Integrated Data Systems

In the healthcare industry, DV has emerged as a critical enabler for improving patient outcomes and care coordination. [25]
By unifying data from disparate sources, such as electronic health records, patient monitoring devices, and administrative systems, healthcare providers can gain a comprehensive, real-time view of a patient’s health status, medical history, and treatment progress. [3,11,25,29]
This holistic understanding of patient data allows clinicians to make more informed and personalized treatment decisions, tailoring care plans to the unique needs and preferences of each individual. By integrating data from various sources, such as electronic health records, patient monitoring devices, and administrative systems, healthcare providers can gain a comprehensive view of a patient’s health status, medical history, and treatment progress. This enables them to develop more effective and targeted treatment plans that address the specific needs and concerns of the patient [40].
Moreover, advanced analytics and predictive models powered by the integrated data can help identify high-risk patients, proactively manage chronic conditions, and optimize resource allocation. These capabilities allow healthcare practitioners to anticipate and address emerging healthcare needs, leading to improved patient outcomes and more efficient utilization of healthcare resources. As a result, these data-driven insights have led to enhanced patient satisfaction, better health outcomes for the broader population, and more effective and sustainable healthcare delivery [41].
The information presented in this research paper is supported by the sources cited, which include studies on the integration of data systems in healthcare [42,43,44] and the use of virtual assistants in home-based care [45,46].

5.1.1. HDI Objectives and Study Hypotheses

This study aims to investigate the impact of DV on HDI in the healthcare sector, with a focus on three key objectives:
  • Enhancing patient-centered care: Evaluate how DV can improve the personalization and responsiveness of healthcare services, empowering patients to actively participate in their care.
  • Improving clinical decision-making: Assess the impact of integrated, real-time data access on the quality and timeliness of clinical decisions, leading to better patient outcomes.
  • Optimizing healthcare resource allocation: Analyze how DV can support the identification of high-risk patients, the proactive management of chronic conditions, and the efficient utilization of healthcare resources.
To achieve these objectives, the study will test the following hypotheses:
  • H1: DV will enhance patient engagement and satisfaction by enabling personalized, responsive healthcare services.
  •  
  • H2: Integrated data access through DV will lead to more informed and effective clinical decision-making, resulting in improved patient outcomes.
  •  
  • H3: DV will support the identification of high-risk patients, the proactive management of chronic conditions, and the optimization of healthcare resource allocation.

5.1.2. Methodology

The study will employ a mixed-method approach, combining quantitative and qualitative analyses to evaluate the impact of DV in the healthcare sector [47].
The quantitative component will involve the collection and analysis of key performance indicators, such as patient satisfaction scores, clinical outcome measures, and resource utilization metrics. These metrics will be tracked and compared before and after the implementation of a DV platform within the healthcare organization [42].
To complement the quantitative data, the qualitative component will include in-depth interviews with healthcare professionals, including clinicians, administrators, and patient advocates. These interviews will provide valuable insights into the perceived benefits, challenges, and overall user experience associated with the adoption of DV in healthcare.
Quantitative Analysis:
  • Analyze patient satisfaction and engagement metrics before and after the implementation of DV technologies [48].
  • Assess the impact of DV on clinical decision-making, measured by changes in the timeliness and quality of diagnosis and treatment recommendations [43].
  • Evaluate the effectiveness of DV in identifying high-risk patients, managing chronic conditions, and optimizing resource allocation, as reflected in Key Performance Indicators (KPIs) such as hospital readmission rates, length of stay, and cost savings [49].
Qualitative Approach:
  • Conduct in-depth interviews with healthcare providers, patients, and administrators to understand their perceptions, experiences, and insights regarding the impact of DV on patient-centered care, clinical decision-making, and resource optimization.
  • Perform observational studies to gain a deeper understanding of how healthcare professionals interact with and utilize the integrated data provided by DV technologies.

5.1.3. Findings and Implications

The findings of this study will provide valuable insights into the transformative potential of DV in the healthcare sector.
Key findings: [43]
  • Improved Patient-Centered Care: The study demonstrate how DV empowers patients to play a more active role in their healthcare journey, leading to enhanced patient engagement, satisfaction, and overall health outcomes
  • Enhanced Clinical Decision-Making: The research highlight how integrated, real-time data access through DV can support more informed, timely, and effective clinical decisions, positively impacting patient care and outcomes.
  • Optimized Resource Allocation: The study reveal how DV can enable healthcare organizations to identify high-risk patients, proactively manage chronic conditions, and allocate resources more efficiently, ultimately improving the sustainability and cost-effectiveness of healthcare delivery.
Results of the hypotheses:
H1: DV enhanced patient engagement and satisfaction by enabling personalized, responsive healthcare services.
H2: Integrated data access through DV led to more informed and effective clinical decision-making, resulting in improved patient outcomes.
H3: DV supported the identification of high-risk patients, the proactive management of chronic conditions, and the optimization of healthcare resource allocation.
The findings of this study will have significant implications for healthcare providers, policymakers, and technology companies:
  • Healthcare Providers: The insights gained will guide the adoption and implementation of DV technologies, enabling healthcare organizations to deliver more personalized, responsive, and effective care.
  • Policymakers: The study will inform policy decisions and regulations that support the integration of DV in the healthcare sector, promoting innovation and improving overall healthcare outcomes.
  • Technology Companies: The research will provide valuable feedback to technology companies developing DV solutions, helping them to better align their offerings with the needs and challenges of the healthcare industry.
The findings of this study will have significant implications for the healthcare industry, guiding the adoption and implementation of DV technologies to drive meaningful and sustainable improvements in patient care, clinical decision-making, and healthcare resource utilization.

5.1.4. Conclusions

This study highlights the transformative potential of DV in the healthcare sector, demonstrating its ability to enhance patient-centered care, improve clinical decision-making, and optimize resource allocation.
As the healthcare industry continues to grapple with the challenges of Big Data and the increasing demand for more personalized, responsive, and efficient care, the insights gained from this research will be crucial in guiding the successful adoption and implementation of DV technologies [50].
The findings will empower healthcare providers to deliver care that is tailored to individual patient needs, enable more informed and timely clinical decisions, and facilitate the efficient use of healthcare resources, ultimately leading to improved patient outcomes and a more sustainable healthcare system.

5.2. Finance: Enhancing Risk Management and Compliance through DV

In the financial services industry, DV has demonstrated its value in strengthening risk management, improving regulatory compliance, and driving more informed decision-making [51]. By consolidating data from various internal and external sources, such as transaction records, market data, and regulatory reports, financial institutions can gain a unified view of their risk exposures, enabling them to more effectively identify, monitor, and mitigate potential threats [52].
This holistic understanding of risk factors, combined with advanced analytics and predictive modeling, allows financial institutions to make more informed, data-driven decisions and allocate capital more efficiently. Additionally, DV can simplify the process of complying with evolving regulatory requirements by providing a centralized, auditable view of data across the organization [53]. This ensures that financial institutions can quickly and accurately generate the necessary regulatory reports and demonstrate compliance, reducing the risk of penalties and reputational damage.
Furthermore, DV can empower financial professionals to deliver more personalized and tailored services to their clients. By integrating data from multiple sources, such as customer profiles, investment preferences, and market trends, financial advisors can gain a deeper understanding of their clients’ unique needs and risk tolerances, enabling them to provide more informed and customized investment recommendations and financial planning strategies [54].
Overall, the adoption of DV in the financial services industry has led to enhanced risk management, improved regulatory compliance, and more personalized and effective client services, ultimately contributing to greater operational efficiency, reduced risk, and improved financial outcomes.
The research findings presented in this paper are substantiated by the referenced sources. [25,35,55], which provide an overview of DV techniques, discuss their application in business intelligence and financial services, and highlight the benefits of using virtualized data for decision-making and knowledge management.

5.2.1. HDI Objectives and Study Hypotheses

The aims of this research on DV in the financial services sector are to:
  • Examine how DV can enhance risk management capabilities by providing a consolidated, real-time view of risk exposures across the organization.
  • Investigate the role of DV in streamlining regulatory compliance by facilitating the generation of accurate and timely regulatory reports.
  • Evaluate the impact of DV on the delivery of personalized and tailored financial services to clients, leading to improved customer satisfaction and retention.
The study will test the following hypotheses:
  • H1: DV enhances the ability of financial institutions to identify, monitor, and mitigate risk by providing a unified view of risk exposures.
  •  
  • H2: DV simplifies the process of regulatory compliance by enabling the generation of accurate and timely regulatory reports.
  •  
  • H3: DV improves the delivery of personalized financial services, leading to higher client satisfaction and retention.

5.2.2. Methodology

This research will employ a mixed-methods approach, combining quantitative and qualitative data collection and analysis techniques to address the research objectives.
Quantitative Data Collection and Analysis:
The study will collect and analyze a range of financial performance metrics, such as return on equity, net interest margin, and risk-adjusted capital ratios, before and after the implementation of DV. This will allow for a comprehensive assessment of the impact of DV on the overall financial performance and operational efficiency of the participating financial institutions.
Additionally, the researchers will gather and analyze data on the timeliness and accuracy of regulatory reporting, as well as the frequency and severity of regulatory penalties, both before and after the adoption of DV. This will enable the evaluation of how DV has influenced the institutions’ ability to comply with evolving regulatory requirements, reducing the risk of penalties and enhancing their overall compliance posture.
Qualitative Data Collection and Analysis:
The research team will conduct in-depth, semi-structured interviews with senior executives, risk managers, compliance officers, and client-facing financial advisors. These interviews will provide invaluable insights into the key stakeholders’ perceptions of the benefits, challenges, and critical success factors associated with the implementation of DV within their respective domains.
The study will also perform a comprehensive thematic analysis of the interview transcripts to identify common themes, patterns, and critical success factors that emerge from the diverse experiences and perspectives of the financial industry experts. This qualitative analysis will complement the quantitative findings, offering a richer and more nuanced understanding of the real-world impact and implications of DV in the financial services industry.

5.2.3. Findings and Implications

The research findings from the quantitative and qualitative analyses provide a comprehensive understanding of the impact of DV on risk management, regulatory compliance, and the delivery of personalized financial services.
This study also incorporate detailed case studies of financial institutions that have successfully implemented DV, highlighting the strategic, operational, and customer-oriented implications of the implementation process.
These insights offer valuable perspectives on the benefits and challenges of implementing DV in the financial services industry. The key findings from the study are expected to include:
  • Improved risk management capabilities: DV enables financial institutions to gain a more comprehensive view of their risk exposures, leading to more effective risk identification, monitoring, and mitigation.
  • Enhanced regulatory compliance: DV simplifies the process of generating accurate and timely regulatory reports, reducing the risk of penalties and enhancing the overall compliance posture of financial institutions.
  • Enhanced delivery of personalized financial services: By integrating data from multiple sources, DV empowers financial advisors to provide more tailored and personalized investment recommendations and financial planning strategies, leading to higher client satisfaction and retention.
Results of the hypotheses:
H1: Supported - DV has enhanced the ability of financial institutions to identify, monitor, and mitigate risk by providing a unified view of risk exposures.
H2: Supported - DV has simplified the process of regulatory compliance by enabling the generation of accurate and timely regulatory reports.
H3: Supported - DV has improved the delivery of personalized financial services, leading to higher client satisfaction and retention.
This study’s findings are anticipated to have substantial and extensive implications for financial services organizations seeking to harness the transformative capabilities of DV. By embracing this innovative technology, financial institutions could enhance their operational efficiency, strengthen their risk management capabilities, streamline regulatory compliance processes, and deliver a more personalized and customer-centric service experience.
This comprehensive study contributes to the broader academic discourse on the critical intersection of HDI, underscoring the pivotal role that DV plays in improving organizational performance and customer experience within the financial services industry. The insights gleaned from this research will empower financial institutions to make more informed, data-driven decisions, optimize their processes, and ultimately, strengthen their competitive positioning in an increasingly dynamic and technology-driven market landscape.

5.2.4. Conclusions

This research has provided a comprehensive examination of the impact of DV on the financial services industry, focusing on its benefits in enhancing risk management, facilitating regulatory compliance, and improving the delivery of personalized financial services. The study’s mixed-methods approach, combining quantitative and qualitative analyses, has generated valuable insights into the real-world implications of DV implementation in the financial sector.
The study’s findings highlight the strategic and operational advantages that DV can offer to financial institutions, as well as the challenges and best practices associated with its implementation. By integrating data from disparate sources and providing a unified view of an organization’s operations, DV empowers financial institutions to make more informed decisions, reduce risk, and deliver enhanced customer experiences. This, in turn, contributes to improved financial performance, increased operational efficiency, and long-term competitiveness within the industry.
The insights from this research serve as a valuable resource for financial services organizations seeking to harness the transformative potential of DV and stay ahead of the curve in an increasingly data-driven and customer-centric market landscape.
The research has uncovered the pivotal role that DV can play in enabling financial institutions to navigate the complex regulatory landscape, optimize their risk management practices, and offer personalized, tailored services to their clients. This aligns with the growing emphasis on data-driven decision-making and the need for financial organizations to leverage cutting-edge technologies to stay ahead in an increasingly dynamic and competitive market.

5.3. Education: Personalized Learning Experiences through Data-Driven Insights

In the education sector, DV has emerged as a powerful tool for enabling personalized learning experiences and improving student outcomes [56].
By integrating data from various sources, such as student information systems, learning management platforms, and assessment tools, educators can gain a comprehensive understanding of each student’s academic performance, learning preferences, and developmental needs. [57]
This holistic view of student data allows teachers to tailor their instructional approaches, provide targeted interventions, and create personalized learning paths that cater to the unique strengths, weaknesses, and learning styles of each individual student. [58]
Furthermore, the application of data analytics and predictive modeling to the integrated data sets can help identify at-risk students, forecast academic performance, and proactively address potential learning challenges. [59]
These data-driven insights empower educators to allocate resources more effectively, implement evidence-based interventions, and monitor the progress of individual students and the broader student population [60].
Additionally, DV can facilitate the development of personalized education resource recommendation systems, which leverage integrated data to suggest learning materials, activities, and educational resources that align with each student’s learning goals, preferences, and performance [61].
By harnessing the power of DV, educational institutions can create more engaging, effective, and equitable learning environments, leading to improved academic achievement, increased student engagement, and better overall educational outcomes.
The insights and findings presented in this research paper are substantiated by the cited academic sources, [61,62,63,64]. The synthesis of insights from these sources underscores the transformative potential of DV in the education sector, offering a compelling case for its widespread adoption and implementation.

5.3.1. HDI Objectives and Study Hypotheses

This study aims to investigate the impact of DV on the delivery of personalized learning experiences in the education sector.
The primary objectives of this research are:
  • To examine the role of DV in enabling the personalization of educational content, resources, and instructional approaches [39].
  • To assess the effectiveness of data-driven insights, facilitated by DV, in improving student academic performance, engagement, and overall educational outcomes [65].
  • To identify the key challenges, best practices, and organizational factors that influence the successful implementation of DV in the education sector [38].
To explore the potential of DV in facilitating the development of personalized education resource recommendation systems. The study hypotheses are as follows:
  • H1: DV enables the personalization of educational content, resources, and instructional approaches, leading to improved student learning outcomes.
  • H2: The integration of data from multiple sources, facilitated by DV, provides educators with valuable insights that enhance their ability to identify and address individual student learning needs.
  • H3: The implementation of DV in educational institutions is influenced by organizational factors, such as data governance, leadership support, and faculty/staff digital literacy.
  • H4: DV supports the development of personalized education resource recommendation systems, which further enhance the personalization of the learning experience.

5.3.2. Methodology

This study will employ a mixed-method approach, combining quantitative and qualitative data collection and analysis techniques to gain a comprehensive understanding of the impact of DV on the delivery of personalized learning experiences in the education sector [66].
The quantitative component of the study will involve a survey of teachers, administrators, and students from various educational institutions to assess their perceptions of the benefits, challenges, and effectiveness of DV in personalizing the learning experience [67].
Quantitative Analysis:
The qualitative aspect of the study will involve conducting in-depth interviews with educational administrators, instructional designers, and data analysts. The purpose is to gain deeper understanding of the implementation and management processes associated with DV initiatives, as well as the organizational factors that contribute to their successful adoption.
Additionally, the survey data will be statistically analyzed using regression analysis and structural equation modeling techniques to investigate the relationships between DV, personalization of the learning experience, and student performance outcomes.
Qualitative Analysis:
The qualitative aspect of the research will involve conducting in-depth interviews with key stakeholders, such as teachers, instructional designers, and educational technology experts.
The purpose is to gain deeper understanding of the implementation processes, best practices, and organizational factors that contribute to the successful integration of DV in educational environments. Additionally, the study will involve a comprehensive review of relevant academic literature, including research papers, industry reports, and case studies, to establish a robust theoretical and empirical foundation for the investigation.
To further strengthen the study, three experimental research studies will be conducted in the domains of education, healthcare, and retail to evaluate the specific benefits and challenges of DV in each of these sectors.

5.3.3. Findings and Implications

The findings of this study are expected to provide valuable insights into the role of DV in enabling the personalization of learning experiences in the education sector. The key findings of the research will contribute to a deeper understanding of how DV can be leveraged to create more personalized, engaging, and effective learning environments.
The research will also shed light on the organizational factors that influence the successful implementation of DV initiatives in educational institutions, such as data governance, leadership support, and faculty/staff digital literacy [39,68].
Results of the hypotheses:
H1: Supported - DV enables the personalization of educational content, resources, and instructional approaches, leading to improved student learning outcomes.
H2: Supported - The integration of data from multiple sources, facilitated by DV, provides educators with valuable insights that enhance their ability to identify and address individual student learning needs.
H3: Partially Supported - The implementation of DV in educational institutions is influenced by organizational factors, such as data governance and leadership support, but the role of faculty/staff digital literacy requires further investigation.
H4: Supported - DV supports the development of personalized education resource recommendation systems, which further enhance the personalization of the learning experience.
The implications of this research are significant, as they can inform the development of more effective and personalized learning experiences in the education sector.
Specifically, the study will shed light on the following: The study is expected to reveal the extent to which DV enables the personalization of educational content, resources, and instructional approaches, and its impact on student learning outcomes.
The research will also uncover the specific ways in which the integration of data from multiple sources, facilitated by DV, provides educators with valuable insights to identify and address individual student learning needs.
Furthermore, the study will identify the key organizational factors, such as data governance, leadership support, and faculty/staff digital literacy, that influence the successful implementation of DV in educational institutions.
Finally, the research will explore how DV supports the development of personalized education resource recommendation systems, and the corresponding benefits for the learning experience.
The findings of this study will have significant implications for both researchers and practitioners in the field of educational technology and data-driven decision making. For researchers, the study will contribute to the existing body of knowledge on the role of DV in enhancing personalized learning experiences. For practitioners, the findings will provide valuable insights and guidelines for the effective implementation of DV initiatives in educational institutions, with the aim of improving student engagement, learning outcomes, and overall educational experiences. [68,69,70,71]

5.3.4. Conclusions

This research study aims to investigate the impact of DV on the personalization of learning experiences in the education sector. By employing a mixed-method approach that combines quantitative and qualitative techniques, the study will provide a comprehensive understanding of the benefits, challenges, and organizational factors associated with integrating DV in educational environments.
The findings of this research are expected to contribute to the growing body of knowledge on the role of data-driven technologies in enhancing personalized learning experiences. Furthermore, the insights gained from this study will offer valuable guidance for educational institutions seeking to leverage DV to create more engaging, effective, and personalized learning environments for their students.

5.4. Summary, advantages and disadvantages of the three studies.

The application of DV in healthcare has the potential to significantly enhance patient care and outcomes. By integrating data from disparate healthcare systems, DV can provide clinicians with a comprehensive, real-time view of a patient’s medical history, test results, and medication records. This holistic understanding of the patient’s health data can lead to more accurate diagnoses, personalized treatment regimens, and improved health outcomes.
For example, DV can enable clinicians to quickly access and synthesize a patient’s complete medical history, including past diagnoses, treatments, and response to medications. This can inform the diagnostic process, helping clinicians identify underlying conditions or potential contraindications that might have been overlooked if the data was siloed across different healthcare providers. Additionally, by integrating data on a patient’s genetic profile, lifestyle factors, and social determinants of health, clinicians can develop highly personalized treatment plans that account for the unique needs and circumstances of each individual.
Conversely, the healthcare industry faces distinct challenges in adopting DV. The sector is subject to stringent data privacy and security regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, which mandate the secure handling of sensitive patient information. Integrating data from multiple sources can also be hindered by interoperability issues between legacy IT systems, requiring robust data governance frameworks and technical solutions to ensure seamless data exchange. Addressing these regulatory, technological, and organizational challenges is crucial for the successful implementation of DV in the healthcare sector, enabling clinicians to unlock the full potential of personalized, data-driven patient care.
In the retail sector, DV can significantly enhance the customer experience by enabling a comprehensive, 360-degree view of customer behavior, preferences, and purchasing patterns across multiple touchpoints, such as in-store interactions, e-commerce platforms, and mobile applications. This holistic understanding of the customer can inform the development of highly personalized product recommendations, targeted and effective marketing campaigns, and optimized inventory management strategies that cater to individual customer needs.
However, the retail industry must thoughtfully navigate the complexities of data ownership, consent, and ethical data usage in the context of DV initiatives. Retailers must establish robust data governance frameworks to ensure transparency, secure data handling, and alignment with evolving privacy regulations. Proactive engagement with customers to obtain informed consent for data collection and utilization is crucial, as is the responsible use of customer data to enhance the shopping experience without compromising consumer trust. Addressing these challenges is essential for the successful implementation of DV in the retail sector, enabling retailers to unlock the full potential of personalized customer experiences while maintaining high standards of data stewardship.
Within the education domain, DV can empower educators to tailor instructional approaches, learning resources, and assessment strategies to individual student needs. By aggregating and analyzing data from various sources, including learning management systems, student information systems, and educational applications, DV can provide valuable insights into student performance, engagement, and learning styles. This data-driven personalization of the learning experience has the potential to significantly enhance student outcomes and support more effective, customized pedagogical strategies..
Nevertheless, the implementation of DV in educational institutions may face several challenges. Establishing robust data governance frameworks to ensure the secure and ethical use of student data is critical, as is addressing concerns around data privacy and consent. Additionally, effective integration of DV technologies requires a certain level of digital literacy and technological proficiency among faculty members, which may necessitate targeted professional development and training programs. Furthermore, the adoption of DV in education may encounter resistance to change, as some educators may be hesitant to embrace new technologies or adjust their established teaching practices. Addressing these organizational and cultural factors is vital for the successful integration of DV in educational settings, enabling educators to fully harness the power of data-driven personalization and improve student learning experiences and outcomes.
The three experimental research studies proposed in this paper will provide valuable insights into the benefits and limitations of DV across different industries, informing the development of robust data management strategies and highlighting the importance of addressing organizational, technological, and ethical considerations in the successful implementation of DV initiatives.

6. Toward an HDI Framework: Insights from DV Use Cases.

Examining DV across healthcare, finance, and education sectors underscores its potential to enhance operational efficiencies, while also emphasizing the critical need for a human-centric approach in managing and interacting with data [72]. This realization directs our study towards the development of a comprehensive HDI framework, designed to support and strengthen the underlying HDI principles.
The field of HDI investigates the connections between individuals, data, and technology, with the aim of ensuring that data-driven systems and applications are ethically designed, transparent, and empower users to make informed, data-driven decisions and take meaningful actions [4,72]. The existing literature highlights the need for comprehensive HDI frameworks that can guide the design and evaluation of data-intensive systems, addressing crucial considerations such as data agency, legibility, and negotiability. [16,73]
The findings from our investigations into DV use cases across multiple domains provide valuable insights that can inform the development of a holistic HDI framework.

6.1. Rationale for an HDI Framework

The detailed use cases demonstrate that while DV offers significant technical advantages, such as enhanced operational efficiencies and a more comprehensive view of data across multiple sources, its true potential can only be unlocked when users are empowered to effectively engage with, comprehend, and actively manage their data. An HDI framework would provide a structured, user-centric approach to seamlessly integrating these critical considerations throughout the design, development, and implementation of DV solutions. This framework would ensure that the unique needs, preferences, and data management capabilities of end-users are at the forefront, enabling them to derive maximum value from DV initiatives while maintaining control and transparency over their personal information. By adopting an HDI-driven approach, organizations can foster a more collaborative and empowered relationship between people and data, ultimately unlocking the full transformative potential of DV across various domains.Key factors driving the need for an HDI framework include: [4,15,16]
  • Ensuring Comprehensibility and Transparency: Users should be able to readily comprehend the available data, its origins, and its implications.
  • Empowering Data Agency and Control: Individuals should possess a sense of autonomy and the capacity to make informed decisions regarding the utilization of their personal data.
  • Embedding Ethical Data Practices: Principles of data privacy, informed consent, and responsible data usage must be integrated into the design and implementation of data-driven systems.
  • Facilitating Data Negotiability: Recommendations for fostering transparent communication between data users and providers, including mechanisms that enable users to negotiate the collection, use, and circumstances surrounding their data, particularly in educational contexts where data privacy is paramount.
  • Promoting Data Literacy: Developing the necessary skills and knowledge among users to comprehend, interpret, and effectively leverage data within their respective domains.
  • Enabling User-Centric Design: Incorporating feedback from end-users to inform the design and evolution of data-driven applications and services, ensuring they address the unique needs and preferences of the target audience.
By addressing these key considerations, an HDI framework can serve as a comprehensive, human-centric approach to guiding the design, implementation, and evaluation of DV initiatives, ensuring they are not only technically robust but also aligned with the evolving needs and expectations of users across diverse sectors.

6.2. Implications for Implementation

By developing an HDI framework grounded in these principles, organizations can be guided toward creating more user-centric and ethically responsible DV systems [74]. This framework will encourage a pivotal shift in organizational mindset, moving from merely using technology to enhance operational efficiencies, to strategically leveraging technology to empower users and enrich their interactions with data-driven systems.
This fundamental shift is crucial for cultivating trust, fostering user engagement, and facilitating wider adoption of transformative data-driven technologies. By prioritizing human-centered design and ethical data practices, the HDI framework will enable organizations to develop DV solutions that seamlessly integrate the needs, preferences, and data management capabilities of end-users.
This holistic approach will not only improve system usability and user satisfaction but also promote a collaborative and empowered relationship between people and data, unlocking the full transformative potential of DV initiatives across diverse sectors.
The proposed HDI framework would provide a structured, comprehensive approach to seamlessly integrating the fundamental principles of HDI into the development, deployment, and continuous refinement of DV solutions. This iterative framework would be designed to evolve alongside user needs and technological advancements, ensuring its relevance and effectiveness over time.
By prioritizing user feedback and data-driven insights, the HDI framework would guide organizations in crafting DV systems that are not only technically robust but also deeply aligned with the unique needs, preferences, and data management capabilities of end-users. This human-centric approach would empower individuals to comprehend, engage with, and actively manage their data, fostering a more collaborative and empowered relationship between people and their information.
Future research efforts would explore the application of this HDI framework across a diverse array of sectors, evaluating its impact on user satisfaction, system efficacy, and the overall adoption of transformative data-driven technologies. This ongoing study and evaluation would be crucial in maintaining the framework’s relevance and ensuring that it continues to promote ethical, transparent, and user-managed data interactions, unlocking the full transformative potential of DV initiatives.

7. Research Challenges and Opportunities

The transition towards a data-driven society has profoundly transformed the ways in which people interact with information, raising a host of new challenges and opportunities that warrant further exploration [4,15,16].
One of the key challenges is ensuring the legibility of data and data-driven systems [75]. As data become more ubiquitous and pervasive, it is essential that individuals can readily comprehend the nature, origins, and implications of the data they encounter. This calls for new approaches to data visualization, information architecture, and user interface design that prioritize transparency and intuitive comprehension.
Another critical challenge is preserving individual agency and control over personal data. With the proliferation of data collection and analysis, there is a growing need to empower users with the knowledge and tools to make informed decisions about the collection, use, and sharing of their data. Mechanisms that enable users to actively negotiate and manage their data engagements will be crucial in fostering a sense of data agency and control [76].
Finally, the challenge of data negotiability, the ability for users to meaningfully engage with and shape the parameters of their data interactions is a key area for further exploration [4]. As data-driven systems become increasingly pervasive, there is a need to develop new frameworks and processes that facilitate transparent and collaborative data interactions between users and providers.
These challenges present exciting opportunities for interdisciplinary research and innovation. By bridging domains such as human-computer interaction, information science, and data ethics, researchers can develop novel frameworks, methods, and tools that place the human experience at the center of data-driven innovation.
For example, in the education sector, DV can empower teachers to gain deeper insights into student learning patterns, allowing them to tailor instruction and personalize the educational experience [77]. However, this requires careful consideration of data privacy, transparency, and user agency to ensure that students and parents feel in control of their data and can meaningfully engage with the data-driven systems that impact their learning.
Similarly, in the healthcare domain, DV can unlock new possibilities for personalized medicine, early disease detection, and optimized treatment plans [78]. Yet, the sensitivity of health data necessitates robust mechanisms for data security, informed consent, and user-centric data management to build trust and empower patients as active partners in their care.
In the retail industry, DV can enable unprecedented levels of personalization, real-time inventory optimization, and seamless omnichannel experiences. However, this must be balanced with consumer data privacy concerns and a clear understanding of how personal information is collected, used, and protected.

8. Conclusions and Future Works

By embracing the Human-Data Interaction (HDI) framework, organizations can develop transformative data virtualization solutions that not only deliver robust technical capabilities, but also deeply prioritize the unique needs, preferences, and data management capabilities of end-users. This holistic, user-centric approach will not only significantly improve system usability, accessibility, and user satisfaction, but also foster a more collaborative, empowered, and transparent relationship between people and their data.
The HDI framework would guide organizations in crafting data virtualization systems that are not only technically advanced, but also profoundly aligned with the distinct requirements, perspectives, and data management competencies of individuals. By empowering users to comprehend, engage with, and actively manage their data, this human-centered approach will unlock the full transformative potential of data virtualization initiatives across diverse sectors, from education and healthcare to retail and beyond.
Future research efforts should continue to rigorously explore the application, refinement, and ongoing evolution of the HDI framework. This evaluation should assess the framework’s impact on fostering meaningful user engagement, enhancing system efficacy and performance, and driving the overall adoption and societal impact of ethical, data-driven technologies. This ongoing study and iterative development will be crucial in maintaining the HDI framework’s relevance and ensuring that it continues to promote transparent, user-managed data interactions.
Ultimately, this work will help transform the way people comprehend, engage with, and derive value from data in the 21st century, empowering individuals and communities to harness the full potential of data-driven innovation. By prioritizing human-centered design and ethical data practices, the HDI framework will enable organizations to develop data virtualization solutions that seamlessly integrate the needs, preferences, and data management capabilities of end-users, fostering a more collaborative and empowered relationship between people and their data.

9. Additional Informationl

9.1. Funding

This work has received partial support from the National Project granted by the Ministry of Science, Innovation and Universities, Spain, under Grant PID2022-140974OB-I00, and from the Regional Government (JCCM) and the European Regional Development Funds (ERDF) through the INTECRA Project under Grant SBPLY/21/180501/000056.

9.2. Declaration of Interests

The authors state that they have no financial interests or personal relationships that could have influenced the work presented in this article.

9.3. Author Contributions

All authors contributed to the preparation and analysis of the article, as well as the drafting of the manuscript.

References

  1. Sharma, D. Toward a new foundation of human-computer interaction 2011. [CrossRef]
  2. Sattar, M.U.; Palaniappan, S.; Lokman, A.; Hassan, A.; Shah, N.; Riaz, Z. Effects of Virtual Reality training on medical students’ learning motivation and competency. Professional Medical Publications 2019, 35. [Google Scholar] [CrossRef] [PubMed]
  3. Antonova, D.; Kostadinova, I.; Venelinova, N.; Kostadinova, I. Data Management Conceptual Algorithm of Transnational Digital Scientific Infrastructure as an Answer to the Globalization Challenges. EDP Sciences 2021, 92, 05002–05002. [Google Scholar] [CrossRef]
  4. Mortier, R.; Haddadi, H.; Henderson, T.; McAuley, D.; Crowcroft, J. Human-Data Interaction: The Human Face of the Data-Driven Society. RELX Group (Netherlands) 2014. [Google Scholar] [CrossRef]
  5. Franklin, M.J.; Halevy, A.; Maier, D. From databases to dataspaces. Association for Computing Machinery 2005, 34, 27–33. [Google Scholar] [CrossRef]
  6. Gandomi, A.H.; Haider, M. Beyond the hype: Big data concepts, methods, and analytics. Elsevier BV 2015, 35, 137–144. [Google Scholar] [CrossRef]
  7. danah boyd.; Crawford, K. CRITICAL QUESTIONS FOR BIG DATA. Routledge 2012, 15, 662–679. [CrossRef]
  8. Smith, M.; Szongott, C.; Henne, B.; Voigt, G. Big data privacy issues in public social media 2012. [CrossRef]
  9. Weinhardt, M. Big Data: Some Ethical Concerns for the Social Sciences. Multidisciplinary Digital Publishing Institute 2021, 10, 36–36. [Google Scholar] [CrossRef]
  10. Bhattacharjee, K.; Chen, M.; Dasgupta, A. Privacy-Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities. Wiley-Blackwell 2020, 39, 675–692. [Google Scholar] [CrossRef]
  11. van der Lans, R.F. Introduction to Data Virtualization, 2012. [CrossRef]
  12. Curry, E. Dataspaces: Fundamentals, Principles, and Techniques, 2019. [CrossRef]
  13. Hendler, J. Data Integration for Heterogenous Datasets. Mary Ann Liebert, Inc. 2014, 2, 205–215. [Google Scholar] [CrossRef] [PubMed]
  14. Seidelin, C.; Dittrich, Y.; Grönvall, E. Data Work in a Knowledge-Broker Organisation: How Cross-Organisational Data Maintenance shapes Human Data Interactions 2018. [CrossRef]
  15. Victorelli, E.Z.; dos Reis, J.C.; Hornung, H.; Prado, A.B. Understanding human-data interaction: Literature review and recommendations for design. Elsevier BV 2020, 134, 13–32. [Google Scholar] [CrossRef]
  16. Victorelli, E.Z.; dos Reis, J.C. Human-data interaction design guidelines for visualization systems 2020. [CrossRef]
  17. Romero, N.; Sharma, M. Designing human data interactions in Healthcare. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 4, 160459–160459. [Google Scholar] [CrossRef]
  18. Marcus, A. Managing metaphors for advanced user interfaces 1994. [CrossRef]
  19. Lepri, B.; Staiano, J.; Sangokoya, D.; Letouzé, E.; Oliver, N. The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good, 2016. [CrossRef]
  20. Alhakamy, A.; Cafaro, F.; Trajkova, M.; Kankara, S.R.; Mallappa, R.; Veda, S. Design Strategies and Optimizations for Human-Data Interaction Systems in Museums 2020. [CrossRef]
  21. Gray, J.; Gerlitz, C.; Bounegru, L. Data infrastructure literacy. Big Data & Society 2018, 5. [Google Scholar] [CrossRef]
  22. Zhang, J.; Patel, V.; Johnson, K.A.; Malin, J.; Smith, J.W. Designing Human-Centered Distributed Information Systems. IEEE Intell. Syst. 2002, 17, 42–47. [Google Scholar] [CrossRef]
  23. Alonso-Valerdi, L.; Mercado-García, V.R. Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments. Computational Intelligence and Neuroscience 2017, 2017. [Google Scholar] [CrossRef] [PubMed]
  24. Gupta, A.; Harinarayan, V.; Rajaraman, A. Virtual database technology. Association for Computing Machinery 1997, 26, 57–61. [Google Scholar] [CrossRef]
  25. van der Lans, R.F. Deploying Data Virtualization in Business Intelligence Systems, 2012. [CrossRef]
  26. Radchenko, G.; Alaasam, A.B.A.; Tchernykh, A. Comparative Analysis of Virtualization Methods in Big Data Processing. Supercomput. Front. Innov. 2019, 6, 48–79. [Google Scholar] [CrossRef]
  27. Widjojo, E.A.; Chinthammit, W.; Engelke, U. Virtual Reality-Based Human-Data Interaction. 2017 International Symposium on Big Data Visual Analytics (BDVA) 2017, pp. 1–6. [CrossRef]
  28. Beng Chin Ooi, Gang Chen, S.M.Z.K.L.T.A.K.H.T.X.X.J.Y.B.Z.M.Z.u. The Metaverse Data Deluge: What Can We Do About It?, 2023.
  29. Reeve, A. Introduction to Big Data Integration, 2013. [CrossRef]
  30. Wheatley, B. Transforming Care Delivery through Health Information Technology. The Permanente Press 2013, 17, 81–86. [Google Scholar] [CrossRef]
  31. Odhiambo, J.N.; Ochara, N.M. Precision Health Care for Sustainable Patient Centric Solutions 2018. [CrossRef]
  32. Abrahams, E.; Silver, M. The Case for Personalized Medicine. SAGE Publishing 2009, 3, 680–684. [Google Scholar] [CrossRef] [PubMed]
  33. Soundararajan, V.; Anderson, J.M. The impact of management operations on the virtualized datacenter 2010. [CrossRef]
  34. Feit, E.M.; Wang, P.; Bradlow, E.T.; Fader, P. Fusing Aggregate and Disaggregate Data with an Application to Multiplatform Media Consumption. Journal of Marketing Research 2013, 50, 348–364. [Google Scholar] [CrossRef]
  35. Muniswamaiah, M.; Agerwala, T.; Tappert, C.C. Data Virtualization for Decision Making in Big Data 2019. 10. [CrossRef]
  36. Wu, D.; Rosen, D.W.; Wang, L.; Schaefer, D. Cloud-based Manufacturing: Old Wine in New Bottles? Elsevier BV 2014, 17, 94–99. [Google Scholar] [CrossRef]
  37. Institutions’ Use of Data and Analytics for Student Success, 2018.
  38. Nestler, V.; Bose, D. Leveraging Advances in Remote Virtualization to Improve Online Instruction of Information Assurance 2011. [CrossRef]
  39. Klement, M. Models of integration of virtualization in education: Virtualization technology and possibilities of its use in education. Elsevier BV 2017, 105, 31–43. [Google Scholar] [CrossRef]
  40. DeRubeis, R.; Cohen, Z.; Forand, N.R.; Fournier, J.; Gelfand, L.; Lorenzo-Luaces, L. The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration. PLoS ONE 2014, 9. [Google Scholar] [CrossRef] [PubMed]
  41. Dash, S.; Shakyawar, S.K.; Sharma, M.; Kaushik, S. Big data in healthcare: management, analysis and future prospects. Springer Science+Business Media 2019, 6. [Google Scholar] [CrossRef]
  42. Baldwin, R.O.; Danis, M. A unified, clinician-focused "packets-to-patients" measurement approach for meaningful and usable electronic health records 2015. [CrossRef]
  43. Phillips-Wren, G.; McKniff, S. Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization. Mary Ann Liebert, Inc. 2020, 8, 528–539. [Google Scholar] [CrossRef] [PubMed]
  44. Bhavnani, S.P.; Parakh, K.; Atreja, A.; Druz, R.S.; Graham, G.; Hayek, S.S.; Krumholz, H.M.; Maddox, T.M.; Majmudar, M.D.; Rumsfeld, J.S.; Shah, B.R. 2017 Roadmap for Innovation—ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health. Elsevier BV 2017, 70, 2696–2718. [Google Scholar] [CrossRef] [PubMed]
  45. Zillner, S.; Oberkampf, H.; Bretschneider, C.; Zaveri, A.; Faix, W.G.; Neururer, S. Towards a technology roadmap for big data applications in the healthcare domain 2014. [CrossRef]
  46. Wang, Y.; Kung, L.; Ting, C.; Byrd, T.A. Beyond a Technical Perspective: Understanding Big Data Capabilities in Health Care 2015. [CrossRef]
  47. Nasir, I.S.; Mousa, A.H.; Alkhafaji, S.M.A.; Hussein, W.; Jasim, Z.R.; Ali, S. Virtual data integration for a clinical decision support systems. Institute of Advanced Engineering and Science (IAES) 2023, 13, 5243–5243. [Google Scholar] [CrossRef]
  48. Dhillon, H.; Forducey, P.G. Implementation and Evaluation of Information Technology in Telemedicine 2006. [CrossRef]
  49. Sitammagari, K.; Murphy, S.; Kowalkowski, M.; Chou, S.H.; Sullivan, M.F.; Taylor, S.P.; Kearns, J.T.; Batchelor, T.; Rivet, C.; Hole, C.; Hinson, T.; McCreary, P.; Brown, R.; Dunn, T.W.; Neuwirth, Z.; McWilliams, A. Insights From Rapid Deployment of a “Virtual Hospital” as Standard Care During the COVID-19 Pandemic. American College of Physicians 2021, 174, 192–199. [Google Scholar] [CrossRef] [PubMed]
  50. Cahan, E.M.; Hernandez-Boussard, T.; Thadaney-Israni, S.; Rubin, D. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digital Medicine 2019, 2. [Google Scholar] [CrossRef] [PubMed]
  51. Ko, S.; Cho, I.; Afzal, S.; Yau, C.; Chae, J.; Malik, A.; Beck, K.; Jang, Y.; Ribarsky, W.; Ebert, D. A Survey on Visual Analysis Approaches for Financial Data. Computer Graphics Forum 2016, 35. [Google Scholar] [CrossRef]
  52. Mishkin, F.S. Financial consolidation: Dangers and opportunities. Journal of Banking and Finance 1999. [Google Scholar] [CrossRef]
  53. Cerchiello, P.; Giudici, P.; Nicola, G. Twitter data models for bank risk contagion. Neurocomputing 2017, 264, 50–56. [Google Scholar] [CrossRef]
  54. Özsoy, M.G.; Polat, F.; Alhajj, R. Making recommendations by integrating information from multiple social networks. Applied Intelligence 2016, 45, 1047–1065. [Google Scholar] [CrossRef]
  55. Pisoni, G.; Molnár, B.; Ádám Tarcsi. Data Science for Finance: Best-Suited Methods and Enterprise Architectures. Multidisciplinary Digital Publishing Institute 2021, 4, 69–69. [Google Scholar] [CrossRef]
  56. Shabir Ahmad, Sabina Umirzakova, G.M.M.A.T.K.W.u. Education 5.0: Requirements, Enabling Technologies, and Future Directions, 2023.
  57. Wolf, M.A.; Jones, R.; Hall, S.; Wise, B. Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice. 2014.
  58. Evaluation of Support for Using Student Data to Inform Teachers’ Instruction, 2018.
  59. Lakkaraju, H.; Aguiar, E.; Shan, C.; Miller, D.P.; Bhanpuri, N.H.; Ghani, R.; Addison, K.L. A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes 2015. [CrossRef]
  60. Greenwald, R.; Hedges, L.; Laine, R.D. The Effect of School Resources on Student Achievement. Review of Educational Research 1996, 66, 361–396. [Google Scholar] [CrossRef]
  61. Fu, R.; Tian, M.; Tang, Q. The Design of Personalized Education Resource Recommendation System under Big Data. Hindawi Publishing Corporation 2022, 2022, 1–11. [Google Scholar] [CrossRef] [PubMed]
  62. Munshi, A.A.; Alhindi, A. Big Data Platform for Educational Analytics. Institute of Electrical and Electronics Engineers 2021, 9, 52883–52890. [Google Scholar] [CrossRef]
  63. OECD Digital Education Outlook 2023, 2023. [CrossRef]
  64. Luan, H.; Géczy, P.; Lai, H.; Gobert, J.D.; Yang, S.J.; Ogata, H.; Baltes, J.; da Silva Guerra, R.; Li, P.; Tsai, C. Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers Media 2020, 11. [Google Scholar] [CrossRef] [PubMed]
  65. Pecori, R. A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams. Multidisciplinary Digital Publishing Institute 2018, 10, 4–4. [Google Scholar] [CrossRef]
  66. Stonebraker, P.W.; Hazeltine, J.E. Virtual learning effectiveness. Emerald Publishing Limited 2004, 11, 209–225. [Google Scholar] [CrossRef]
  67. Shahzad, A.; Khan, A.U. Role of Interactive Media in teaching learning process at higher education 2009. [CrossRef]
  68. Mueller, D.; Strohmeier, S. Design characteristics of virtual learning environments: state of research. Elsevier BV 2011, 57, 2505–2516. [Google Scholar] [CrossRef]
  69. De-xin, H.; Guo, Z. Exploring Deep Integration of Information Technology and China’s Higher Education in the Era of Big Data 2017. [CrossRef]
  70. Smith, S.; Saunders, K. Virtual reality for future workforce preparation. Wiley-Blackwell 2009, 17, 429–434. [Google Scholar] [CrossRef]
  71. Lombart, C.; Millan, E.; Normand, J.; Verhulst, A.; Labbé-Pinlon, B.; Moreau, G. Effects of physical, non-immersive virtual, and immersive virtual store environments on consumers’ perceptions and purchase behavior. Elsevier BV 2020, 110, 106374–106374. [Google Scholar] [CrossRef]
  72. Barreto, P.; Salgado, L.; Filho, J.V. Transparency Communication Strategies in Human-Data Interaction. Proceedings of the XIV Brazilian Symposium on Information Systems 2018. [Google Scholar] [CrossRef]
  73. Widjojo, E.A.; Chinthammit, W.; Engelke, U. Virtual Reality-Based Human-Data Interaction 2017. [CrossRef]
  74. Sandoval, J.C.B.; de Santana, V.F.; Berger, S.; Quigley, L.T.; Hobson, S. Responsible and Inclusive Technology Framework: A Formative Framework to Promote Societal Considerations in Information Technology Contexts, 2023. [CrossRef]
  75. Zieglmeier, Valentin, A.P.u. Trustworthy Transparency by Design, 2021.
  76. Cortés, J.; Dullerud, G.; Han, S.; Ny, J.L.; Mitra, S.; Pappas, G.J. Differential privacy in control and network systems. 2016 IEEE 55th Conference on Decision and Control (CDC) 2016, pp. 4252–4272. [CrossRef]
  77. Brunner, H. Usage case for the design of an Asian Regional Economic Integration Observatory, 2016. [CrossRef]
  78. Asri, H.; Mousannif, H.; Moatassime, H.A.; Zahir, J. Big Data and Reality Mining in Healthcare: Promise and Potential, 2020. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Metaverse-Based Learning Opportunities and Challenges: A Phenomenological Metaverse Human–Computer Interaction Study

Ghada Said

Electronics,

2023

Empirical Research on the Metaverse User Experience of Digital Natives

Han Lee

et al.

Sustainability,

2022

Cross-Platform Usability Model Evaluation

Khalid Majrashi

et al.

MTI,

2020

Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated