A user-centric framework to improve the reusability

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsOpen data has a profound effect in working environments in which information is created and shared at all levels. At the local government level, open-data initiatives have resulted in higher levels of transparency as regards policies. Greater engagement between decision-makers and citizens has changed the way data analysis and evidence are used to support local governance. Initiatives on open data are currently playing an essential role in local governments. However, the current challenge of local open data that authorities are facing has gradually changed from accessibility issues to measures of the impact of the ongoing open-data projects, from more data catalogs to sustainable and increasing levels of reuse of released data, and better reusability of open data. Despite an increasing amount of data being made open, few studies have looked into its level of reusability, and the barriers that hamper the reuse of open geodata from a data consumer’s perspective are an issue that most communities of data users are currently faced with. Some frameworks are showing how the level of maturity in national open-data initiatives is either increasing or decreasing, but there is still a need for a specific framework to guide local data authorities to engage their current users and also help them to move toward a bottom-up approach. This research contributes with three elements in this regard. The first is the current status of the level of reuse of open geodata in cities. This is followed by a taxonomy of the barriers faced by data users in Colombia and Spain, and the third is a set of elements that shape a user-centric framework to help data authorities improve the level of reuse of published open geodata in their ongoing local initiatives. The proposed taxonomy and framework are based on a literature review, an online survey, and a set of participatory workshops conducted in four selected cities (Bogotá, Medellín, Cali in Colombia and Valencia in Spain), with local data authorities and user communities from different backgrounds and with experience in the field of open data. The taxonomy presented in this research highlights a number of issues such as outdated data, low integration of data producers, and difficulty to access data, the most relevant from the data consumer’s point of view being misinterpretation and misuse of released data and their terms of use. Once the barriers had been identified and validated with data users across the selected cities, this research defined the elements included in a conceptual framework that local authorities could use as a guideline to improve the level of reuse in their ongoing open data initiatives. The core elements of this framework are what are defined as ’Impact Enablers’, which consist of three aspects considered by the literature reviewed as relevant to improve the positive impact of current initiatives. These three factors are: A) the requirements of data-user communities; B) open data at city level as a way to promote and engage users; and finally, C) a geographic approach to improving the level of reusability of released data due to its potential to engage more users. The second part of the proposed framework is made up of four connected elements: 1) The complete identification of data-user communities and their needs; 2) The community of reuse as a set of technological tools to promote the reusability of released data; 3) User-focused metadata; and 4) Reuse-focused legal terms. The elements mentioned earlier were compiled and included due to their relevance for data-user communities in the four use cases included in this research. This framework provides a clear path for local data authorities to reshape their current open data strategies so as to include data-user requirements and move toward a bottom-up approach. The research ends with a discussion and some concluding points, in addition to several limitations in the application of our findings. At the end of this dissertation, a roadmap for future research and implementations are presented, taking into account some reflections on the framework

The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16

Big Data in Bioeconomy

This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Digitale Edition in Österreich

Between 2016 and 2020 the federally funded project "KONDE - Kompetenznetzwerk Digitale Edition" created a network of collaboration between Austrian institutions and researchers working on digital scholarly editions. With the present volume the editors provide a space where researchers and editors from Austrian institutions could theorize on their work and present their editing projects. The collection creates a snapshot of the interests and main research areas regarding digital scholarly editing in Austria at the time of the project