Data-driven World Research Articles

A Narrative Review of Identity, Data and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing

As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers.

Comparative Analysis of ARIMA, SARIMA and Prophet Model in Forecasting

Machine learning has become a powerful tool in forecasting, offering greater accuracy than traditional human predictions in today’s data-driven world. The capability of machine learning to predict future trends has significant implications for key sectors such as finance, healthcare, and supply chain management. In this study, ARIMA/SARIMA (AutoRegressive Integrated Moving Average/Seasonal AutoRegressive Integrated Moving Average), alongside Prophet, a scalable forecasting tool developed by Facebook based on a generalized additive model, are considered. These models are applied to predict the demand for antidiabetic drugs. The records were collected by the Australian Health Insurance Commission. This dataset was sourced from Medicare Australia. The study evaluates the performance of these models based on their Mean Absolute Error (MAE), a key metric for assessing forecast accuracy. Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are also considered. The outcome of the comparative analysis shows that the Prophet model outperformed both ARIMA and SARIMA models, achieving an MAE of 0.74, which is significantly lower than the MAE values of 2.18 and 3.02 obtained by SARIMA and ARIMA, respectively. Prophet's superior performance shows its effectiveness in handling complex, non-linear trends and seasonal patterns often observed in real-world time series data. This research contributes to the growing knowledge of machine learning-based forecasting and shows the importance of advanced models like Prophet in optimizing business operations and driving innovation. The findings from this research offer valuable guidance for data experts, analysts, and researchers in selecting the best forecasting methods for reliable predictions.

The Privacy-Centric Data Pipeline : Strategies for Implementing Robust Security Measures in Data Engineering

This article presents a comprehensive framework for addressing data privacy and security challenges in modern data engineering environments. As organizations grapple with ever-increasing volumes of sensitive data and an evolving regulatory landscape, data engineers face complex challenges in safeguarding information throughout its lifecycle. We explore key components of a robust data protection strategy, including regulatory compliance, technical safeguards, and organizational practices. Our analysis covers critical areas such as encryption methodologies, access control mechanisms, data masking techniques, and lifecycle management policies. We also examine the role of data governance frameworks and the importance of continuous employee training in maintaining a secure data ecosystem. By synthesizing current best practices and emerging trends, this article provides data engineers and organizations with actionable insights for implementing effective privacy and security measures. Our findings underscore the need for a holistic approach that integrates technical solutions with strong governance practices to ensure data integrity, confidentiality, and compliance in an increasingly data-driven world.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

AI powered privacy protection: A survey of current state and future directions

The research is conducted to investigate how AI transforms the notion of protection of privacy through discussing the status quo technologies, challenges, and future directions. All due to the sudden rise of digital data, protection of personal information has evolved as a major concern for which AI-enabled solutions are being introduced. Advanced concepts of AI are therefore marking the paradigm shift in how organizations handle sensitive data, letting more secure and privacy-oriented practices lead the way with notions such as differential privacy, federated learning, and anomaly detection. However, despite these advances, formidable challenges remain regarding the opacity of AI models, possible algorithmic biases, and regulatory compliance. The paper further discusses the future of AI for privacy protection, with new developments: XAI, integration of AI with blockchain, and quantum-resistant cryptography. These advances offer great transparency, security, and responsibility in privacy management. It further underlines that the collaboration of governments, industrial leaders, and researchers is required in providing appropriate frameworks for the usage of AI, given the ethical and regulatory concerns around privacy protection as the influence of AI grows. While AI is indeed very promising in improving privacy protections, the degree to which it can actually function depends on the surmounting of present limitations and harmonization of technological development with shifting data privacy criteria. The AI in this research paper will continue to play a leading role in shaping the future of privacy preservation, with answers continuing to emanate from innovation, security, and ethical concerns. It is through continuous improvement and collaboration that AI can ensure effective privacy measures in an increasingly data-driven world.

Leveraging Power BI for Enhanced Data Visualization and Business Intelligence

In today’s data-driven world, businesses must harness the power of data to remain competitive and make informed decisions. Power BI, a leading business analytics tool from Microsoft, offers comprehensive capabilities for transforming raw data into meaningful insights. This paper explores the benefits of leveraging Power BI for enhanced data visualization and business intelligence. It emphasizes how Power BI’s interactive dashboards, customizable reports, and real-time data updates empower organizations to gain deeper insights and foster data-driven decision-making. By integrating data from various sources, Power BI allows users to visualize trends, patterns, and anomalies through its intuitive and user-friendly interface. Moreover, its AI-powered features and advanced analytics tools make predictive analytics more accessible, driving future strategies. This paper also examines the collaborative potential of Power BI, as teams can easily share reports and insights across departments. As businesses increasingly adopt digital transformation strategies, Power BI stands out as a vital tool for improving operational efficiency, understanding market trends, and driving business growth. This study highlights the significance of Power BI in supporting organizations to not only interpret complex data but also to stay agile and responsive in an evolving business environment. In conclusion, Power BI’s robust data visualization and business intelligence capabilities offer organizations a competitive edge in today’s fast-paced, data-centric landscape.

Advancing New Governance Models for Gender Data in Climate Resilience Funding

Abstract Globally, between USD 850 to USD 940 billion was allocated for 2021 climate finance. However, a mere 0.01 percent of this was directed towards projects addressing both climate change and women’s rights. Unfortunately, the climate crisis is not gender-neutral. Yet, gender data analysis and the significant role of women in climate action are often overlooked, resulting in critical issues being underrepresented in climate budgets. In today’s data-driven world, gender data assumes a critical role in highlighting women’s significant contributions to climate change, thereby influencing funding decisions. Within this context, the Room 9 sub-group of the “17 Rooms initiative” is currently exploring the potential of data cooperatives to facilitate financing for women’s groups transitioning towards climate-resilient practices. This paper explores the intersection of gender data and climate resilience funding and advocates for improved governance models that ensure accurate, comprehensive, and meaningful representation of gender data in climate funding decision-making processes.

Elevating sentiment analysis with VGG-16's facial expression insights

<span lang="EN-US">In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional Neural Network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviours. To improve emotion recognition reliability and reactivity, this study examines Random Forest, Support Vector Machine (SVM), and CNN methodologies. The VGG-16 CNN model outperforms over SVM and Random Forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.</span>

Toward Constructing Frameworks for Task- and Design-Optimized Visualizations.

Visualization is crucial to augment and enhance human understanding and decision-making in today's data-driven world. However, the way data are visualized can influence and drastically change the conclusions people draw using data. The findings around visualization effectiveness are nuanced, and guidelines for effective visualization design depend on the visual channels used, chart types, and analysis tasks. This points to a significant need to understand the intersection of these factors to create optimized visualizations. We need a framework to define this intersection that fills the gap by providing a task-optimized visualization design for better quality and higher decision-making confidence that gives designers objective guidance. A task-optimized visualization design framework strategically integrates visual channels, visualization types, and specific low-level tasks to enhance data interpretation and optimize user task performance. We discuss constructing a visualization framework that considers both human perception for encoding techniques and the task being performed, enabling optimizing visualization design to maximize efficiency. Furthermore, we highlight a task-optimized framework's impact and potential applications.

The Role of Artificial Intelligence Technology and Innovation in Disrupting Traditional Business Models and Startup Ecosystem

The rapid advancement of Artificial Intelligence (AI) is revolutionizing entrepreneurship by creating unprecedented opportunities and driving transformative change across various sectors. This abstract explores how AI is influencing entrepreneurial ventures, focusing on its role in enhancing data analytics, personalizing customer experiences, and advancing healthcare and automation. AI enables startups to leverage sophisticated tools for predictive analytics, tailored interactions, and efficient operations, which can disrupt traditional business models and accelerate growth. As AI technology continues to progress, it reshapes the competitive landscape, offering new avenues for innovation and strategic advantage. Understanding and harnessing AI’s potential is becoming crucial for entrepreneurs seeking to thrive in an increasingly digital and data-driven world. Keywords: Artificial intelligence technology, innovation, startup ecosystem, entrepreneurship

Data science education in undergraduate physics: Lessons learned from a community of practice

It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned from integrating data science into undergraduate physics education. In this article, we present insight and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.

Building Scalable Data Warehouses: Best Practices and Case Studies

In today's data-driven world, the ability to manage, store, and analyze large volumes of data is crucial for business success. The demand for scalable data warehouses has risen dramatically as organizations seek to handle the explosion of data generated by modern applications and digital transactions. "Building Scalable Data Warehouses: Best Practices and Case Studies" explores the key strategies, methodologies, and technologies involved in designing and implementing scalable data warehouses that meet the demands of today and the future. The paper highlights the importance of architecture choices, data modeling techniques, and performance optimization in creating data warehouses that can grow with an organization’s needs. Additionally, it provides case studies that demonstrate the real-world application of these principles in various industries, showing how scalable data warehouses have enabled companies to maintain high performance, reduce costs, and enhance decision-making capabilities. The paper begins by defining what constitutes a scalable data warehouse, emphasizing the importance of a flexible and adaptive architecture that can accommodate growing data volumes and changing business requirements. It explores different architectural approaches, including the benefits and challenges of traditional on-premises data warehouses versus cloud-based solutions. The paper also discusses the role of data modeling, including dimensional and normalized approaches, in ensuring that the data warehouse can handle increasing complexity without compromising performance.

The role of data science in transforming business operations: Case studies from enterprises

Data science has emerged as a pivotal force in transforming business operations across various industries, driving innovation, operational efficiency, and strategic decision-making. This review paper explores the multifaceted role of data science in business, examining key concepts, historical integration, and strategic advantages. It discusses the application of data science in diverse business domains, highlighting techniques such as predictive analytics, sentiment analysis, and optimization that have revolutionized marketing, supply chain management, finance, and customer service. The paper further analyzes data science's tangible and intangible benefits, including cost reduction, improved customer experience, and enhanced productivity, which collectively contribute to a competitive edge in the market. The paper reflects on emerging trends like artificial intelligence, machine learning, ethical data practices, and the integration of blockchain and IoT, which are set to shape the future of data-driven business operations. It offers recommendations for businesses to prepare for this evolving landscape, emphasizing the importance of a data-centric culture, robust infrastructure, and collaboration. The paper concludes by underscoring the critical role of data science in fostering sustained business success in an increasingly data-driven world. Keywords: Data Science, Business Transformation, Predictive Analytics, Operational Efficiency, Artificial Intelligence, Strategic Decision-Making.

A Survey on Balancing Data Loss Prevention (DLP) with User Privacy in a Data-Driven World

A Survey on Balancing Data Loss Prevention (DLP) with User Privacy in a Data-Driven World

Harnessing marketing analytics for enhanced decision-making and performance in SMEs

In the contemporary business landscape, small and medium-sized enterprises (SMEs) are increasingly leveraging marketing analytics to enhance decision-making and improve performance. This paper explores the transformative impact of marketing analytics on SMEs, emphasizing its role in driving data-driven decisions that optimize marketing strategies, enhance customer engagement, and boost overall business performance. Through the systematic collection and analysis of data, SMEs can gain deeper insights into customer behaviors, preferences, and trends, enabling more targeted and effective marketing campaigns. The implementation of marketing analytics facilitates the measurement of key performance indicators (KPIs) such as click-through rates, conversion rates, and return on investment (ROI), providing a robust framework for evaluating the effectiveness of marketing initiatives. By utilizing various types of analytics, including descriptive, predictive, and prescriptive analytics, SMEs can make informed decisions that not only respond to current market conditions but also anticipate future trends and opportunities. Moreover, the adoption of marketing analytics supports the personalization of customer experiences, allowing SMEs to deliver tailored content and offers that resonate with specific audience segments. This level of personalization fosters deeper customer relationships and increases the likelihood of conversions and customer loyalty. The continuous monitoring and analysis of marketing data enable SMEs to remain agile and responsive to changing market dynamics, ensuring that their marketing strategies remain relevant and effective. By embracing a data-driven approach, SMEs can minimize the risk of ineffective marketing efforts and allocate resources more efficiently, ultimately driving sustainable growth and competitive advantage. In conclusion, harnessing marketing analytics is crucial for SMEs aiming to enhance decision-making and performance. The ability to derive actionable insights from data empowers SMEs to optimize their marketing strategies, improve customer engagement, and achieve better business outcomes. As SMEs continue to navigate an increasingly data-driven world, the strategic use of marketing analytics will be pivotal in securing long-term success and growth.

Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world’s most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.

Practical and theoretical judgment in data-driven financial due diligence

PurposeNew digital technologies are reshaping the business landscape and accounting work. This paper aims to investigate how incorporating more data and new data analytics (DA) tools impacts the role and use of judgment in financial due diligence (FDD).Design/methodology/approachThe paper reports findings from a field study at a Big Four accounting firm in Sweden (“DealCo”). The primary data includes semi-structured interviews, observations and other meetings. Theoretically, it draws on Dewey’s The Logic of Judgments of Practise and Logic: The Theory of Inquiry and distinguishes between theoretical (what is probably true) and practical judgment (what to do).FindingsIn DealCo’s FDD practice, using more data and new DA tools meant that the realm of possibility had expanded significantly. To manage the newfound abundance and to use DA effectively, DealCo’s advisors invoked practical and theoretical judgments in different stages and areas of the data-driven FDD. The paper identifies four critical uses of judgment: Setting priorities and exercising restraint (practical judgment) and forming hypotheses and doing sense checks (theoretical judgment). In these capacities, practical judgment and theoretical judgment were essential in transforming raw data into actionable insights and, in effect, an indeterminate situation into a determinate one.Originality/valueThe study foregrounds the practical dimension of knowledge production for decision-making and contributes to a better understanding of the role, use and importance of accounting professionals’ judgment in a data-driven world.

Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review.

Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations. Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities? We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI. This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability. While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.

Developing an Avatar-Based Framework for Unified Client Identification in Banking Systems Using Generative AI and Graph Neural Networks

In today’s data-driven world, the accurate and consistent identification of clients across multiple platforms is a critical challenge for financial institutions, government comptroller departments, and third-party service providers. Fragmented and inconsistent data across various systems pose significant risks, including regulatory non-compliance, fraud, and operational inefficiencies. This thesis presents the development of an innovative avatar-based framework for unified client identification in banking systems, leveraging the power of Generative AI and Graph Neural Networks (GNNs). The framework synthesizes disparate client data into a single, cohesive representation in the virtual world, effectively addressing the challenges of data fragmentation and inconsistency. Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are employed to enhance data quality by generating realistic synthetic data and imputing missing values. These models augment the existing datasets, ensuring completeness and accuracy of client profiles. Simultaneously, GNNs are utilized to model the complex relationships and interactions within the client data, capturing intricate dependencies and enhancing the accuracy of client identification. The proposed framework offers substantial benefits, including improved regulatory compliance, enhanced operational efficiency, and superior customer experience. By providing a unified view of client data, financial institutions can better detect and prevent fraudulent activities, meet stringent regulatory requirements, and deliver personalized services. Government comptroller departments can ensure more effective public fund management and maintain transparency. Third-party service providers can leverage accurate client profiles for better service delivery and risk management. The race to implement such advanced frameworks is a pivotal factor in determining leadership in the financial and administrative sectors. Institutions that adopt and integrate this technology swiftly will gain a significant competitive advantage, setting new standards in client identification and data management. This research underscores the transformative potential of combining Generative AI and GNNs in creating a robust, scalable, and efficient system for unified client identification, paving the way for future advancements in this critical field.

Automatic Data Repair: Are We Ready to Deploy?

Data quality is paramount in today's data-driven world, especially in the era of generative AI. Dirty data with errors and inconsistencies usually leads to flawed insights, unreliable decision-making, and biased or low-quality outputs from generative models. The study of repairing erroneous data has gained significant importance. Existing data repair algorithms differ in information utilization, problem settings, and are tested in limited scenarios. In this paper, we compare and summarize these algorithms with a driven information-based taxonomy. We systematically conduct a comprehensive evaluation of 12 mainstream data repair algorithms on 12 datasets under the settings of various data error rates, error types, and 4 downstream analysis tasks, assessing their error reduction performance with a novel but practical metric. We develop an effective and unified repair optimization strategy that substantially benefits the state of the arts. We conclude that, it is always worthy of data repair. The clean data does not determine the upper bound of data analysis performance. We provide valuable guidelines, challenges, and promising directions in the data repair domain. We anticipate this paper enabling researchers and users to well understand and deploy data repair algorithms in practice.