The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific req...The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.展开更多
Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing...Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.展开更多
In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterpr...In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterprises and government institutions.Effective data governance is a crucial tool for maximizing data value and mitigating data risks.This article examines the application of data governance models in the digital economy,aiming to offer technical insights and guidance for data-driven enterprises and governments in China.By elevating their data governance standards in the new era,this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy,ultimately driving the continued growth of both the digital economy and society.展开更多
In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework coveri...In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.展开更多
With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaboratio...With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.展开更多
Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted colle...Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.展开更多
at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service....at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.展开更多
The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive st...The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.展开更多
The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of dat...The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.展开更多
The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governan...The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governance,the legal regulation of such administrative activities should pursue the value goal of balancing the use and protection of personal information,promoting the efficient,safe,and orderly flow of personal information,and forming a standardized order for the use of personal information,and build a systematic public governance mechanism.From the perspective of reflective law,the personal information governance of automated administration shall promote its self-regulation under the stimulation of external regulation.Fundamental rights are a combination of dual reflective structures of the political system and the legal system,forming the constitutional basis for the self-regulation of automated administration.The personal information protection system and digital administrative law are external regulations from the legal system,forming the legal basis and providing automated administration with scenario-based and classified governance ideas.The government data scenario focuses on the standardized governance of personal information processing activities.It takes the principles of fair information practice as its basic framework and the necessity to perform statutory duties as its legal basis.The algorithmic decision-making scenario forms its framework system based on the principle of algorithmic due process,which clarifies the information subject’s right not to be subject to automated decision-making and the mechanism for exercising the rights.It establishes the information flow order in public algorithms based on the procedural law regulations of prior algorithm designs.展开更多
Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulati...Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.展开更多
Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and dist...Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and distributed cooperating.Their purposes are to solve and improve the governance efficiency of dealing with public health and security incidents at the executive level.However,there are still many deficiencies in the current data governance and collaborative governance of joint prevention and control systems,which are mainly reflected in incomplete data collection,unimpeded data sharing,inflexible collaborative cooperation,and inadequate collaborative supervision.Therefore,a new innovative governance model is urgently needed.Blockchain technology is suitable for implementing multiparty data sharing and cooperation,and at the same time,it supports penetrating supervision and management.This paper studies the blockchain model for joint governance of public health and security incidents.It focuses on the multiagent collaborative prevention and control governance model,which provides a new opportunity for model innovation in data governance and in cooperative governance.展开更多
Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach...Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.展开更多
Enterprise application integration encounters substantial hurdles,particularly in intricate contexts that require elevated scalability and speed.Transactional applications directly accessed by many systems frequently ...Enterprise application integration encounters substantial hurdles,particularly in intricate contexts that require elevated scalability and speed.Transactional applications directly accessed by many systems frequently overload databases,undermining process efficiency.This paper examines the utilization of data lakes-historically used for data analysis-as a centralized integration layer that accommodates various temporalities and consumption modalities.The sug-gested method diminishes system interdependence and the burden on transac-tional databases,enhancing scalability and data governance in both mono-lithic and distributed frameworks.展开更多
This article summarizes the practical model of data governance in universities through the study of academic literature and cases and analyses the problems existing in current data governance practices.By comparing th...This article summarizes the practical model of data governance in universities through the study of academic literature and cases and analyses the problems existing in current data governance practices.By comparing the characteristics of different data architectures,this study proposes a scenario-based governance architecture for lakehouse integration,deploys it in a university case environment,conducts functional verification and performance testing,and demonstrates its applicability in supporting diverse governance scenarios through the analysis of two specific scenarios,further illustrating the effectiveness of lakehouse architecture based on BF integration in supporting data governance.展开更多
National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,buil...National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,building and topographic information.Integration of local datasets may result in a dataset satisfying the needs of users of national datasets,but is it productive for those who create and maintain the data?This article presents a stakeholder analysis of the Basisregistratie Adressen en Gebouwen(BAG),a collection of base information about addresses and buildings in the Netherlands.The information is captured and maintained by municipalities and integrated into a national base register by Kadaster,the Cadastre,Land Registry and Mapping Agency of the Netherlands.The stakeholder analysis identifies organisations involved in the BAG governance framework,describes their interests,rights,ownerships and responsibilities in the BAG,and maps the relationships between them.Analysis results indicate that Kadaster and the municipalities have the highest relative importance in the governance framework of the BAG.The study reveals challenges of setting up a governance framework that maintains the delicate balance between the interests of all stakeholders.The results provide guidance for SDI role players setting up governance frameworks for national or global datasets.展开更多
The field of machine learning(ML)is sufficiently young that it is still expanding at an accelerating pace,lying at the crossroads of computer science and statistics,and at the core of artificial intelligence(AI)and da...The field of machine learning(ML)is sufficiently young that it is still expanding at an accelerating pace,lying at the crossroads of computer science and statistics,and at the core of artificial intelligence(AI)and data science.Recent progress in ML has been driven both by the development of new learning algorithms theory,and by the ongoing explosion in the availability of vast amount of data(often referred to as"big data")and low-cost computation.The adoption of ML-based approaches can be found throughout science,technology and industry,leading to more evidence-based decision-making across many walks of life,including healthcare,biomedicine,manufacturing,education,financial modeling,data governance,policing,and marketing.Although the past decade has witnessed the increasing interest in these fields,we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience.In this paper,we present a comprehensive view on geo worldwide trends(taking into account China,the USA,Israel,Italy,the UK,and the Middle East)of ML-based approaches highlighting the rapid growth in the last 5 years attributable to the introduction of related national policies.Furthermore,based on the literature review,we also discuss the potential research directions in this field,summarizing some popular application areas of machine learning technology,such as healthcare,cyber-security systems,sustainable agriculture,data governance,and nanotechnology,and suggest that the"dissemination of research"in the ML scientific community has undergone the exceptional growth in the time range of 2018–2020,reaching a value of 16,339 publications.Finally,we report the challenges and the regulatory standpoints for managing ML technology.Overall,we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains,as well as serve as a reference point for both academia and industry professionals,particularly from a technical,ethical and regulatory point of view.展开更多
Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment effi...Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.展开更多
Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.D...Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust.The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data sovereignty.This paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data.The work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data quality.The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.展开更多
文摘The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.
文摘Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.
文摘In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterprises and government institutions.Effective data governance is a crucial tool for maximizing data value and mitigating data risks.This article examines the application of data governance models in the digital economy,aiming to offer technical insights and guidance for data-driven enterprises and governments in China.By elevating their data governance standards in the new era,this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy,ultimately driving the continued growth of both the digital economy and society.
基金Special Project for Renovation and Procurement of Donghua University,Ministry of Education,China (No. CG202002845)。
文摘In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.
文摘With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.
基金the National Natural Science Foundation of China(No.62272480 and 62072470)。
文摘Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.
文摘at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant number:101109045)National Key R&D Program of China with Grant number 2018YFB1800804+2 种基金the National Natural Science Foundation of China(Nos.NSFC 61925105,and 62171257)Tsinghua University-China Mobile Communications Group Co.,Ltd,Joint Institutethe Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03)。
文摘The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.
文摘The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.
基金2021 National Social Science Fund of China Major Project“Research on the Social Impact of Internet Platforms and Its Governance Path”(Project approval Number 21&ZD195)the“Research on Legal Issues of Anti-terrorism Legislation and Human Rights Protection”under Shandong Provincial Youth Innovation Team Development Program for Universities(Project Number 2022RW016)of the Shandong Provincial Department of Education in 2022。
文摘The processing of data such as personal information constitute the infrastructure of automated administration.Under the multi-legal requirements for personal information protection,data security,and algorithm governance,the legal regulation of such administrative activities should pursue the value goal of balancing the use and protection of personal information,promoting the efficient,safe,and orderly flow of personal information,and forming a standardized order for the use of personal information,and build a systematic public governance mechanism.From the perspective of reflective law,the personal information governance of automated administration shall promote its self-regulation under the stimulation of external regulation.Fundamental rights are a combination of dual reflective structures of the political system and the legal system,forming the constitutional basis for the self-regulation of automated administration.The personal information protection system and digital administrative law are external regulations from the legal system,forming the legal basis and providing automated administration with scenario-based and classified governance ideas.The government data scenario focuses on the standardized governance of personal information processing activities.It takes the principles of fair information practice as its basic framework and the necessity to perform statutory duties as its legal basis.The algorithmic decision-making scenario forms its framework system based on the principle of algorithmic due process,which clarifies the information subject’s right not to be subject to automated decision-making and the mechanism for exercising the rights.It establishes the information flow order in public algorithms based on the procedural law regulations of prior algorithm designs.
文摘Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.
基金“Shandong Social Science Planning and Research Project”/“Shandong Academy of Social Sciences Innovation Project”(20BCXJ01)Shandong Provincial Major Technology Innovation Projection under Grant 2018CXGC0703.
文摘Joint prevention and control is a social organization model dealing with the governance of public health and security incidents.The governance models should have the features of multiple subjects co-governing and distributed cooperating.Their purposes are to solve and improve the governance efficiency of dealing with public health and security incidents at the executive level.However,there are still many deficiencies in the current data governance and collaborative governance of joint prevention and control systems,which are mainly reflected in incomplete data collection,unimpeded data sharing,inflexible collaborative cooperation,and inadequate collaborative supervision.Therefore,a new innovative governance model is urgently needed.Blockchain technology is suitable for implementing multiparty data sharing and cooperation,and at the same time,it supports penetrating supervision and management.This paper studies the blockchain model for joint governance of public health and security incidents.It focuses on the multiagent collaborative prevention and control governance model,which provides a new opportunity for model innovation in data governance and in cooperative governance.
文摘Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.
文摘Enterprise application integration encounters substantial hurdles,particularly in intricate contexts that require elevated scalability and speed.Transactional applications directly accessed by many systems frequently overload databases,undermining process efficiency.This paper examines the utilization of data lakes-historically used for data analysis-as a centralized integration layer that accommodates various temporalities and consumption modalities.The sug-gested method diminishes system interdependence and the burden on transac-tional databases,enhancing scalability and data governance in both mono-lithic and distributed frameworks.
基金Supported by“2022 Higher Education Science Research Plan Project of the Chinese Higher EducationAssociation,No:22XX0402”“Ministry of Education’s‘ChinaUniversity Industry-University-Research Innovation Fund Special Project’No:2022TX010”.
文摘This article summarizes the practical model of data governance in universities through the study of academic literature and cases and analyses the problems existing in current data governance practices.By comparing the characteristics of different data architectures,this study proposes a scenario-based governance architecture for lakehouse integration,deploys it in a university case environment,conducts functional verification and performance testing,and demonstrates its applicability in supporting diverse governance scenarios through the analysis of two specific scenarios,further illustrating the effectiveness of lakehouse architecture based on BF integration in supporting data governance.
基金Jantien Stoter is funded by the H2020 European Research Council(ERC)under the European Union’s Horizon 2020 Research and Innovation Framework Programme[grant agreement No 677312 UMnD].
文摘National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,building and topographic information.Integration of local datasets may result in a dataset satisfying the needs of users of national datasets,but is it productive for those who create and maintain the data?This article presents a stakeholder analysis of the Basisregistratie Adressen en Gebouwen(BAG),a collection of base information about addresses and buildings in the Netherlands.The information is captured and maintained by municipalities and integrated into a national base register by Kadaster,the Cadastre,Land Registry and Mapping Agency of the Netherlands.The stakeholder analysis identifies organisations involved in the BAG governance framework,describes their interests,rights,ownerships and responsibilities in the BAG,and maps the relationships between them.Analysis results indicate that Kadaster and the municipalities have the highest relative importance in the governance framework of the BAG.The study reveals challenges of setting up a governance framework that maintains the delicate balance between the interests of all stakeholders.The results provide guidance for SDI role players setting up governance frameworks for national or global datasets.
文摘The field of machine learning(ML)is sufficiently young that it is still expanding at an accelerating pace,lying at the crossroads of computer science and statistics,and at the core of artificial intelligence(AI)and data science.Recent progress in ML has been driven both by the development of new learning algorithms theory,and by the ongoing explosion in the availability of vast amount of data(often referred to as"big data")and low-cost computation.The adoption of ML-based approaches can be found throughout science,technology and industry,leading to more evidence-based decision-making across many walks of life,including healthcare,biomedicine,manufacturing,education,financial modeling,data governance,policing,and marketing.Although the past decade has witnessed the increasing interest in these fields,we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience.In this paper,we present a comprehensive view on geo worldwide trends(taking into account China,the USA,Israel,Italy,the UK,and the Middle East)of ML-based approaches highlighting the rapid growth in the last 5 years attributable to the introduction of related national policies.Furthermore,based on the literature review,we also discuss the potential research directions in this field,summarizing some popular application areas of machine learning technology,such as healthcare,cyber-security systems,sustainable agriculture,data governance,and nanotechnology,and suggest that the"dissemination of research"in the ML scientific community has undergone the exceptional growth in the time range of 2018–2020,reaching a value of 16,339 publications.Finally,we report the challenges and the regulatory standpoints for managing ML technology.Overall,we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains,as well as serve as a reference point for both academia and industry professionals,particularly from a technical,ethical and regulatory point of view.
基金supported by the following grants:National Natural Science Foundation of China(Grant No.72271147)Natural Science Foundation of Guangdong Province(Grant No.2022A1515010573)+3 种基金STU Scientific Research Initiation(Grant No.STF21005STF21004)the Philosophy and Social Science Planning Project of Guangdong Province(Grant No.GD21CGL12GD23XYJ76).
文摘Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.
文摘Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust.The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data sovereignty.This paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data.The work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data quality.The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.
