Government Data Openness and Coverage. How do They Affect Trust in European Countries?

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.

Inter-agency government information sharing under data-driven blockchain framework

The inter-agency government information sharing(IAGIS)plays an important role in improving service and efficiency of government agencies.Currently,there is still no effective and secure way for data-driven IAGIS to fulfill dynamic demands of information sharing between government agencies.Motivated by blockchain and data mining,a data-driven framework is proposed for IAGIS in this paper.Firstly,the blockchain is used as the core to design the whole framework for monitoring and preventing leakage and abuse of government information,in order to guarantee information security.Secondly,a four-layer architecture is designed for implementing the proposed framework.Thirdly,the classical data mining algorithms PageRank and Apriori are applied to dynamically design smart contracts for information sharing,for the purposed of flexibly adjusting the information sharing strategies according to the practical demands of government agencies for public management and public service.Finally,a case study is presented to illustrate the operation of the proposed framework.

Open sharing ofgovernment data and enterprise decision making

Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.

Construction of a blockchain-based intelligent platform for government data

The management and application of government data are the bases for the construction of a digital government.Building an intelligent platform for government data by using artificial intelligence,blockchain,and other technical means to achieve open sharing,development,and utilization of data is an important link to promote continuously the construction of a digital government and improve the level of government management and service efficiency.According to the current construction status of government data platforms and the application prospect of blockchain technology,this study proposes to build a blockchain-based intelligent platform for government data.In combination with the technical advantages of blockchain,this study investigates the theory and technical logic of building a blockchain-based intelligent platform for government data and then proposes the theoretical model,architecture,operation mechanism,and core technology of platform construction.This study explores sthe implementation path of building a blockchain-based intelligent platform for government data from five aspects,i.e.,promoting technology upgrading,improving top-level design,strict supervision and regulation,strengthening collaborative research and judgment,and improving technical support.

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

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.

Application of Data Governance Models in the Context of the Digital Economy

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.

Designing and Implementing an Advanced Big Data Governance Platform

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.

College Basic Development Status Data Management System Based on Data Governance Framework

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.

Data Integrity and Risk

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.

Understanding the Importance of Effective Third-Party Risk Management on Data Governance

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.

省级政务大数据平台建设模式研究与启示

建设高质量政务大数据资源平台是实现跨部门、跨区域、跨层级政务信息集成融合,加快建设数字政府、提升公共服务和社会治理等数字智能化水平的重要基础工程。首先,回顾我国电子政务发展历程,并总结出传统省级政务大数据平台的3种建设模式以及存在的问题,即数据鲜活性低、数据一致性差、业务协同管理难、基础支撑力弱和总体投入高。其次,对江苏信用一体化信息资源管控平台进行案例分析,阐述以江苏案例为例的原因,针对传统建设模式存在的问题,提出对应的解决思路和总体架构设计,并梳理出江苏案例的4个借鉴价值。最后,在总结江苏研究和实践经验的基础上,归纳出5点建议,为各省研究、制定省级政务大数据平台的建设指南等政策性文件提供参考。

区域政务服务“跨省通办”政策执行为何受阻——基于长三角地区的案例研究

作为辖区内“一网通办”的延伸与扩展,政务服务“跨省通办”政策执行呈现出较为明显且错综复杂的网络结构。当前,我国“跨省通办”政策执行不仅面临体制性束缚,还遭遇技术性难题。同时,执行过程中各执行主体、执行网络之间的互动以及重要事件都会对政策执行产生影响。因此,推进政务服务“跨省通办”政策的顺利执行,应在加强顶层设计、打破网络边界、坚持网络治理等方面采取措施。

中国政府数据开放的政策创新扩散研究——成本收益和外部压力的竞争性解释

[目的/意义]探讨府际关系对政府数据开放政策创新扩散的影响,并将成本—收益理论和外部压力理论作为竞争性理论,揭示创新扩散的具体机理,为我国大数据发展战略提供可行的政策建议。[方法/过程]选取2014—2018年中国293个地级市的面板数据进行事件史分析,考察成本—收益和外部压力变量的调节效应。[结果/结论]研究结果表明:府际学习和府际竞争对政策采纳具有显著促进作用;相比于“成本—收益”理论,外部压力理论对于政策创新扩散机制的解释力更强,上级政府和社会舆论压力催化了同级政府互动所产生的水平方向上的政策扩散。

A visual analysis approach for data imputation via multi-party tabular data correlation strategies

Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.

PPP背景下控制权配置及其对合作效率影响的模型研究

PPP是公共部门与私人部门合作提供公共品和公共服务的重要模式。本文强调PPP的契约本质,指出合作方特征、产品的公共化程度是PPP合作模式区别于传统的企业与企业合作模式的两大特点,从不完全契约视角出发本研究认为影响PPP合作模式效率的关键是控制权配置。基于这一观点本研究首次通过构建数学模型分析中国背景下PPP合作中的控制权配置与合作效率之间的关系,并指出当存在企业自利性投入的情况下,在不同的参数条件下对公共部门或私人部门赋予不同程度的控制权份额是提高PPP合作效率的关键,同时适当的政府引导能够进一步地提高合作效率。随后的数据模拟也验证了以上结论。

我国政府统计抽样调查的回顾与思考

经过多年的发展,抽样调查已经成为我国政府统计的主要方法之一,并且得到越来越广泛的应用。本文对我国政府抽样调查的发展史进行了详细的回顾和总结,从体制方面和技术方面分析了我国政府现行抽样调查存在的问题,并结合实际情况提出了相应的改革建议。

国外政府开放数据门户服务功能探析

开放数据门户的建设推动了政府信息公开。选取了美国、英国、加拿大、澳大利亚和新加坡五国的政府开放数据门户作为样本,对其数据管理、检索与用户参与功能进行了分析,并希望对我国政府信息公开工作有所借鉴。

区块链:大数据时代破解政府治理数字难题之有效工具

将大数据以及大数据思维嵌入政府治理流程是大数据时代实现政府治理能力和治理体系现代化的重要意旨。但在实际的操作流程中,因数据真实性及低效力问题、数据孤岛阻隔现象以及数据安全性威胁,使得政府面临有效利用大数据进行治理之掣肘。针对多重掣肘,近几年兴起的区块链技术,是P2P组网技术、共识机制、非对称加密技术和智能合约等多种技术的综合创新,在政府治理中具有数据价值化、技术流程去中心化、数据安全化以及新型信用等不同于其他信息技术之属性。通过以上多种技术的组合创新,区块链能够提高数据真实性以创建政府治理的信息基础;能够促进政府各层级各部门之间、政府与社会之间的信任,实现数据共享以提高政府治理的效率;更能够保障数据安全以提高政府治理的合法性和公信力。简言之,如能适当运用,区块链可以成为破解大数据时代政府治理数字难题的有效工具。当然,区块链尚处于起步阶段,其技术的完善和应用效果还需观察,而在政府治理中的作用也不宜盲目夸大。

数据仓库技术在政府决策支持系统中的应用研究

针对传统数据库的决策支持系统存在的数据分散、数据规范不统一、数据可分析能力低等缺点,研究了政府决策支持系统的特殊性,提出了基于数据仓库的政府决策支持系统模型,实现了由原始数据到统一数据的转换、存储和开发,进而实现了辅助决策的功能。为政府在进行科技决策时提供详实、可视、清楚的数据支持。

政府数据开放共享安全保障问题与建议

通过对国内外政府数据开放共享的需求、模式和安全现状进行调研分析,提出政府数据开放共享中政府数据权属关系需明确等安全问题,并从政策法规、标准配套、交换共享平台安全技术体系构建、数据服务能力评估等方面提出安全保障建议,同时构建了包括数据确权、数据分类分级、数据脱敏、安全标记、多级访问控制、数据族谱、安全审计、授权管理等的安全技术和安全管理防护体系,为政府数据开放共享打造"可用不可见"的安全空间。