Big Data Governance Research Articles

Pioneering Museum Data Transformation: Unifying Legacy Systems and Enhancing Data Integrity

In the digital era, museums confront the challenge of modernising legacy data systems to align with current standards. Part of the RECODE (Rethinking Collections Data Ecosystems) Programme (Dupont et al. 2022) examines the complex transition from disparate departmental object models to a unified system, reflecting the broader museum community's struggle with data standardisation. The case study centres on consolidating five distinct data models accumulated over two decades, each tailored to different departmental needs under various constraints. The data mapping process identifies and aligns similar fields across models, facilitating the integration of analogous data points and exposing redundancies and unique practices embedded over years. Major challenges include data complexity, diversity, and quality issues (cleaning, standardizing, and deduplicating), compounded by the massive scale of the data: currently, the Natural History Museum London (NHM) has 262,831,590 records across 8,175 fields, and a total volume of 12 TB of data, which define both the ongoing data modelling work and our future data migration. A key innovation in our process is the immediate visibility of data issues that became apparent upon migrating to Amazon Web Services (AWS). AWS serves as the staging environment for our transition to the new NHM Collections Management System (CMS) and offers an unprecedented platform for directly addressing these challenges. It enables us to query all our data—across all departments and fields—in just seconds. This capability provides a comprehensive view that was previously unattainable. The core of this presentation is sharing "lessons learned" from navigating the intricacies of an ongoing CMS transition within a museum. The endeavor to untangle and unify diverse data models is a common challenge (IEEE Big Data Governance and Metadata Management Industry Connections Activity 2020,Wu et al. 2022, Wu et al. 2021,Little et al. 2022,Woodburn et al. 2022) highlighting the importance of community engagement and knowledge exchange. Our findings underscore the necessity of cross-departmental collaboration and the benefits of a data-driven data modelling approach. By sharing our journey and the developed comprehensive object model (Collier and Woodburn 2022), we aim to contribute valuable insights to museums undergoing similar transformations. The RECODE Programme sheds light on the practical aspects of CMS modernisation through the analytics and knowledge derived from over two decades of collections data and data use.

Harnessing big data for national security: A review of information systems and emerging technologies in the U.S

The rapid growth of big data and emerging technologies has fundamentally transformed the national security landscape, enabling governments to leverage vast amounts of information for decision-making, threat detection, and operational effectiveness. This review explores the integration of big data analytics into U.S. national security systems, focusing on the technological innovations that facilitate real-time data processing, secure communications, and predictive analytics. Key emerging technologies, such as artificial intelligence, blockchain, and the Internet of Things, are evaluated for their role in enhancing intelligence gathering, cybersecurity, and surveillance operations. Furthermore, the paper examines the foundational principles of big data in national security, detailing its data sources, types, and uses in both structured and unstructured forms. While these advancements present significant opportunities, they also pose challenges related to privacy, ethics, policy, and legal frameworks, which are critical for ensuring that the implementation of such technologies aligns with democratic values and security objectives. The paper concludes by highlighting future research directions, emphasizing the need for continued development in big data infrastructure, governance, and ethical considerations to fully harness its potential in protecting national interests.

A conceptual framework for the government big data ecosystem (‘datagov.eco’)

The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a socio-technical network of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports data functions such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, value is created by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.

Research on Transparent Access Technology of Government Big Data

Research on Transparent Access Technology of Government Big Data

Designing and Implementing an Advanced Big Data Governance Platform

Designing and Implementing an Advanced Big Data Governance Platform

Call for Papers: Special Issue on AI-Enhanced Big Data Governance

Call for Papers: Special Issue on AI-Enhanced Big Data Governance

Empowering the Relationship Between the Government Functional Departments and Local Government Integration: An Integrated Study Based on Urban Digital Governance Platforms

Within the context of modernising government governance, the "platform" in the construction of digital government has become key in driving the efficiency of government services and management. This paper, based on the Technology-Organisation-Environment (TOE) framework and combined with theories of holistic governance and collaborative governance, delves into the crucial role of information technology and emerging governance models in eliminating barriers between government departments, optimising departmental structures, and fostering mechanisms for inter-departmental collaboration and sharing. Throughout the research, emphasis is placed on the importance of emerging technologies such as cloud computing, big data, and artificial intelligence in promoting efficient collaboration and resource sharing between government departments. By integrating and optimising governmental internal and inter-departmental data resources and service processes, it is possible to effectively advance the enhancement and integration of the relationship between the government functional departments and local government relations, adapt to the rapid changes in societal demands, and provide more precise and convenient public services. The core objective of this study is to establish a unified government big data platform, promoting the optimisation and integration of departmental structures, constructing an efficient mechanism for departmental collaboration and sharing, achieving data sharing and flow, and establishing a tight governance network. This aims to comprehensively enhance government governance capabilities and service quality, provide assurance for achieving more efficient and equitable governance objectives.

Empowering the Relationship Between the Government Functional Departments and Local Government Integration: An Integrated Study Based on Urban Digital Governance Platforms

Within the context of modernising government governance, the "platform" in the construction of digital government has become key in driving the efficiency of government services and management. This paper, based on the Technology-Organisation-Environment (TOE) framework and combined with theories of holistic governance and collaborative governance, delves into the crucial role of information technology and emerging governance models in eliminating barriers between government departments, optimising departmental structures, and fostering mechanisms for inter-departmental collaboration and sharing. Throughout the research, emphasis is placed on the importance of emerging technologies such as cloud computing, big data, and artificial intelligence in promoting efficient collaboration and resource sharing between government departments. By integrating and optimising governmental internal and inter-departmental data resources and service processes, it is possible to effectively advance the enhancement and integration of the relationship between the government functional departments and local government relations, adapt to the rapid changes in societal demands, and provide more precise and convenient public services. The core objective of this study is to establish a unified government big data platform, promoting the optimisation and integration of departmental structures, constructing an efficient mechanism for departmental collaboration and sharing, achieving data sharing and flow, and establishing a tight governance network. This aims to comprehensively enhance government governance capabilities and service quality, provide assurance for achieving more efficient and equitable governance objectives.

A Survey on Network Security Traffic Analysis and Anomaly Detection Techniques

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

Innovative Cloud Computing Techniques for Big Data Administration Getting Past Roadblocks and Developing New Paths

The digital era's introduction of big data has fundamentally altered the landscape of information management, providing organizations across a wide range of industries with unprecedented opportunities and difficulties. This scientific inquiry digs into the convergence of cloud computing and big data, presenting a thorough history of their evolution and underlining their critical roles in modern data governance. Through a meticulous examination of the critical role of effective big data management across diverse industries, the paper emphasizes the critical importance of cloud computing solutions in addressing the multifaceted complexities arising from data volume, velocity, variety, veracity, and security. Conventional big data handling approaches inside cloud computing frameworks, such as batch processing, stream processing, data warehousing, and virtualization, are thoroughly evaluated in light of their inherent constraints. The paper then examines modern cloud computing solutions that have been precisely tuned to handle the issues of scalability, real-time processing, and security inherent in large data management. The investigation is complemented by the inclusion of informative case studies and real-world examples that demonstrate the practical applicability of these pioneering ideas across a wide range of sectors, from entertainment to manufacturing. Furthermore, the paper defines emerging technologies and trends in cloud computing, highlights persistent obstacles, and outlines prospects for innovation and future research in the field. Ethical and regulatory aspects such as data privacy, fairness, openness, and compliance are extensively reviewed and emphasized. Finally, the article recommends for ongoing innovation, academic investigation, and ethical discernment to fully realize the promise of big data while limiting related hazards, emphasizing the dynamic nature of big data governance within the context of cloud computing. Key Words: Cloud computing, Big data, Hadoop ,Spark, AI and machine learning integration Blockchain

Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.

Big Data-driven Decision Support: Enhancing Information Integration and User Experience with Mobile Integrated Technology

This study examines how big data-driven decision support and mobile technology interact to improve information integration and user experience. The research studies big data for digital decision-making and provides theoretical and practical suggestions to assist organizations in overcoming its challenges. This study used mixed method analysis to find the relationship between big data-driven user experience and mobile-integrated technology. Businesses require sophisticated decision support tools to navigate the digital landscape of massive data. Big data-driven decision support is examined to determine how information integration and user experience affect mobile-integrated technologies. A rigorous quantitative technique examines data volume and decision precision. Although big data volumes may have diminishing returns, decision-making generally improves. The study emphasizes the delicate balance between data volume, quality, velocity, diversity, and governance. Beyond quantitative analysis, the study examines complex decision-making. Information integration methods and user experience affect decision-making time, with more data offering strategic options. Agile integration and user-centric design boost efficiency and decision-making. The research highlights the change in mobile integrated technology. The title fits the research since mobile technology increases information integration and user experience. According to the study, mobile technology's user-friendly gadgets, quick internet connectivity, security safeguards, and app functionality boost user contentment, productivity, and decision-making accuracy. The report also emphasizes big data governance in decision quality. Decision support systems need big data governance for data access, accuracy, security, and compliance. Finally, this study provides theoretical insights into big data-driven decision support and practical suggestions for organizations navigating it. The study uses data, technology, user experience, and governance to improve business decision-making. This provides them with digital-era precision, agility, and strategic edge.

DATA GOVERNANCE IN GAMING INDUSTRY

This article examines the role of Big Data Analytics and Data Governance in the gaming industry. It shows how Big Data Analytics has changed game design and player interaction, focusing on trends and player preferences, especially in free-to-play games. The importance of Data Governance is stressed for handling data responsibly, focusing on challenges like data quality, security, and legal rules like GDPR. Examples from Ubisoft, SEGA, and Kolibri Games show these concepts in action. Metavibes is another example of research in field of Data Governance. The article also looks at how the gaming industry is dealing with data issues, including using strong policies and better security. It predicts future trends in AI and blockchain in gaming. The piece highlights the need for ethical practices, like protecting player privacy, as key for trust in the industry. The article points out the vital impact of these technologies in advancing and growing the gaming world.

In Memory of Dr. David Belanger

Sadly, our dear colleague, Dr. David Belanger, passed away in November last year. David was a founding member of the New Jersey Big Data Alliance (NJBDA)—an alliance of New Jersey academic institutions and corporations that aims to promote Big Data education and research in New Jersey, the parent organization of this journal. “Through the last decade, as our organization grew and expanded its programs, he provided brilliant insight and guidance on our direction, offering suggestions in his thoughtful way and always ready to collaborate. David will be greatly missed,” said Margaret Brennan-Tonetta, NJBDA’s past president and co-founder. At NJBDA, he was most recently Vice President of the Entrepreneurship Committee. David was an internationally known authority on Big Data and data governance. We at NJBDA and JBDAI will continue to remember David as a gentle scholar who cared for people. A colleague fittingly remembered David as being “the kindest scientist of our time.”

The Practical Path of Ruling Colleges and Universities According to Law in the New Era Combined with Big Data Analysis

Abstract With the development of the information age, big data analysis has become the basis for social management and the development of new ideas for intelligent governance. In this paper, the governance process of colleges and universities is presented and stored in the form of data by simulating the content of governance through the full domain data set, and the relationship between different subjects and object features is mapped through the generalized Cartesian product. From there, various data patterns are clustered to effectively manage various types of operations’ linkage. The multi-objective optimization algorithm is used to solve the Pareto optimal solution for different governance subjects, i.e., the mutual coordination between teachers and students. The objective optimization function is established to identify the Pareto frontiers of various governance programs and emergency plans. The results show that in the driving pathway of college big data governance, the correct rate of classification of policing grid governance events in 28 colleges and universities is 0.8279 on average, which effectively improves governance efficiency. Big data governance in colleges and universities enables college governance to continuously move from decentralized to centralized, from part to whole, and to achieve accurate and efficient governance modes.

Digital twins, big data governance, and sustainable tourism

The rapid adoption of digital technologies has revolutionized business operations and introduced emerging concepts such as Digital Twin (DT) technology, which has the potential to predict system responses before they occur, making it an attractive option for smart and sustainable tourism. However, implementing DT software systems poses significant challenges, including compliance with regulations and effective communication among stakeholders, and concerns surrounding security, privacy, and trust with the use of big data. To address these challenges, this paper proposes a documentation framework for architectural decisions (DFAD) that applies the concept of big data governance to the digital system. The framework aims to ensure accountability, transparency, and trustworthiness while adhering to rules and regulations. To demonstrate its applicability, a case study and three case scenarios on the potential use of Mobile Positioning Data (MPD) in Indonesia for DT technology in smart and sustainable tourism were examined. The paper highlights the benefits of DFAD in shaping stakeholder communication and human–machine interactions while leveraging the potential of MPD to measure tourism statistics by Statistics Indonesia since 2016. Not only the documentation framework promotes compliance with regulations, but it also facilitates effective communication among stakeholders and enhances trust and transparency in the use of big data in DT technology for smart and sustainable tourism. This paper emphasizes the importance of effective big data governance and its potential to promote sustainable tourism practices. The multidisciplinarity approach on political science, software engineering, tourism, and official statistics provides an opportunity for academic contribution and decision-making processes.

Perspectives of Collaborative Governance: Integration of Social and Cognitive Computing with Complex Networks

In the current era, big data play a very important role in various government organizations. Therefore, it is necessary to connect government big data with the national modern governance system since it is a crucial resource, asset, technology, and service support facility for government departments. The government information system requires complex system engineering. The collaborative governance of government data by integrating cognitive computing and complex networks is conducive to eliminating the contradiction and tension between policies and policy subjects. It can reduce the cross and overlap of functions between different departments and form a collaborative mechanism of other governance subjects. The suggested mechanism enhanced the government governance content integrity and system synergy. The modernization of the system is also boosted by governance capacity.

The Governance of Big Data and Artificial Intelligence in Network Industries

An important precondition for the development of artificial intelligence (AI) in network industries is the access to big data and the attendant necessities of data sharing and data portability. The goal of this paper is to analyze the changing needs of entrepreneurial decision-making to exhaust the innovation potential of AI-driven big data value chains taking into account AI-specific ethical, security and privacy regulations. The analytical concept of AI-powered big data virtual networks is investigated with a focus on the governance of 5G-based big data value chains required for Internet of Things (IoT) applications in particular smart networks. Although several actors may be involved—such as broadband providers, cloud service providers, geopositioning service providers, or sensor network service providers—the final responsibility for bundling these different service components lies in the hands of the AI-powered big data virtual network providers. In addition to the required data privacy and security regulations, the exploration of new liability rules for AI interacting with traditional technologies is becoming relevant, taking into account AI-specific ethical and transparency obligations. Firstly, the complementary roles of the EU data regulatory framework and European AI regulatory framework are examined. Secondly, the network economic concept of AI-powered big data virtual networks is elaborated taking into account the required regulations. Thirdly, the heterogeneity of AI systems—required for a variety of IoT applications—is considered, with a particular focus on the application of AI within the transportation sector.

Exploring perspectives of research ethics committee members on the governance of big data in sub-Saharan Africa.

Interest in the governance of big data is growing exponentially. However, finding the right balance between making large volumes of data accessible, and safeguarding privacy, preventing data misuse, determining authorship and protecting intellectual property remain challenging. In sub-Saharan Africa (SSA), research ethics committees (RECs) play an important role in reviewing data-intense research protocols. However, this regulatory role must be embedded in a context of robust governance. There is currently a paucity of published literature on how big data are regulated in SSA and if the capacity to review protocols is sufficient. The aim of this study was to provide a broad overview of REC members' awareness and perceptions of big data governance in SSA. A descriptive cross-sectional survey was conducted from April to July 2022. We invited 300 REC members to participate in our online survey via Research Electronic Data Capture (REDCap). A total of 140 REC members, representing 34 SSA countries, completed the online survey. Awareness of data governance laws, policies and guidelines was variable across the subcontinent. A quarter of respondents (25%) indicated that national regulations on the trans-border flow of research data are inadequate. Institutional policies on research data protection were also regarded as being inadequate. Most respondents (64%) believed that they lacked experience in reviewing data-intense protocols. Data governance and regulation in SSA need to be strengthened at both national and institutional levels. There is a strong need for capacity development in the review of data-intense research protocols on the subcontinent.

Development and Application Status of Healthcare Big Data

As informatization progresses in society, the data in the health and medical fields is soaring quickly, which promotes the healthcare big data generated. Healthcare is closely related to each individual's life and health, and healthcare big data has both brought great opportunities and challenges to the health and medical field. Therefore, the development of big data in healthcare is being focused on in the medical and health fields. At present, healthcare big data are being applied in clinical diagnosis, public health, infectious disease prevention, medical research, and drug development. However, these applications are not being used very efficiently nowadays. A large amount of big data is stored, but most of it still cannot be used because of the hazards of storing uniform and different structure data types. This paper uses literature searching to summarize the status quo of healthcare big data at present. Based on the background of big data, this paper describes the sources and characteristics of healthcare big data and figures out the promising applications in the health and medical areas. Moreover, this research also focuses on the data governance of healthcare big data and represents the problems now, such as the barrier between information due to non-uniform data collection methods, and provides development strategies.