A European research roadmap for optimizing societal impact of big data on environment and energy efficiency

We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to allow stakeholders and the big data community to identify and meet big data challenges, and to proceed with a shared understanding of the societal impact, positive and negative externalities, and concrete problems worth investigating. It builds upon a case study focused on the impact of big data practices in the context of Earth Observation that reveals both positive and negative effects in the areas of economy, society and ethics, legal frameworks and political issues. The roadmap identifies European technical and non-technical priorities in research and innovation to be addressed in the upcoming five years in order to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

Perspectives of Integrated “Next Industrial Revolution” Clusters in Poland and Siberia

Rozdział z: Functioning of the Local Production Systems in Central and Eastern European Countries and Siberia. Case Studies and Comparative Studies, ed. Mariusz E. Sokołowicz.The paper presents the mapping of potential next industrial revolution clusters in Poland and Siberia. Deindustrialization of the cities and struggles with its consequences are one of the fundamental economic problems in current global economy. Some hope to find an answer to that problem is associated with the idea of next industrial revolution and reindustrialization initiatives. In the paper, projects aimed at developing next industrial revolution clusters are analyzed. The objective of the research was to examine new industrial revolution paradigm as a platform for establishing university-based trans-border industry clusters in Poland and Siberia47 and to raise awareness of next industry revolution initiatives.Monograph financed under a contract of execution of the international scientific project within 7th Framework Programme of the European Union, co-financed by Polish Ministry of Science and Higher Education (title: “Functioning of the Local Production Systems in the Conditions of Economic Crisis (Comparative Analysis and Benchmarking for the EU and Beyond”)). Monografia sfinansowana w oparciu o umowę o wykonanie projektu między narodowego w ramach 7. Programu Ramowego UE, współfinansowanego ze środków Ministerstwa Nauki i Szkolnictwa Wyższego (tytuł projektu: „Funkcjonowanie lokalnych systemów produkcyjnych w warunkach kryzysu gospodarczego (analiza porównawcza i benchmarking w wybranych krajach UE oraz krajach trzecich”))

Applications Exploiting e-Infrastructures Across Europe and India Within the EU-IndiaGrid Project

In the last few years e-Infrastructures across Europe and India faced remarkable developments. Both national and international connectivity improved considerably and Grid Computing also profited of significant developments. As a consequence scientific applications were in the position of taking substantial benefits from this progress. The most relevant cases are represented by High Energy Physics (with the contribution to the program of Large Hadron Collider at CERN, Geneva) and Nano Science (exploiting NKN-TEIN3-GEANT interconnection for crystallography experiments with the remote access & control of experimental facility at the ESRF Synchrotron based in Grenoble, France directly from Mumbai, India). Other relevant application areas include Climate Change research, Biology, and several areas in Material Science. Within this framework, in the last five years period two specific EU funded projects (the EU-IndiaGrid and EU-IndiaGrid2) played a bridging role supporting several applications that exploited these advanced e-Infrastructures for the benefit of Euro-India common research programs. A crucial important part in the projects activity was the support offered to selected applications which ranges from the training the user communities behind up to the porting of their scientific applications on the grid computing infrastructure. This article aims to present and review the main e Infrastructures development in India and their full exploitation by scientific applications with a focus on the role played by the EUIndiaGrid and EU-IndiaGrid2 projects

Current landscape and influence of big data on finance

Big data is one of the most recent business and technical issues in the age of technology. Hundreds of millions of events occur every day. The financial field is deeply involved in the calculation of big data events. As a result, hundreds of millions of financial transactions occur in the financial world each day. Therefore, financial practitioners and analysts consider it an emerging issue of the data management and analytics of different financial products and services. Also, big data has significant impacts on financial products and services. Therefore, identifying the financial issues where big data has a significant influence is also an important issue to explore with the influences. Based on these concepts, the objective of this paper was to show the current landscape of finance dealing with big data, and also to show how big data influences different financial sectors, more specifically, its impact on financial markets, financial institutions, and the relationship with internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and so on. The connection between big data and financial-related components will be revealed in an exploratory literature review of secondary data sources. Since big data in the financial field is an extremely new concept, future research directions will be pointed out at the end of this study

An Overview of Big Data Analytics in Banking: Approaches, Challenges and Issues

Banks are harnessing the power of Big Data. They use Big Data and Data Science to drive change towards data and analytics to gain an overall competitive advantage. The Big Data has potential to transform enterprise operations and processes especially in the banking sector, because they have huge amount of transaction data. The goal of this paper is to give an overview of different approaches and challenges that exists in Big Data in banking sector. The work presented here will fulfill the gap of research papers in the last five years, with focus on Big Data in central banks and credit scoring in central banks. For this paper, we have reviewed existing research literature, official reports, surveys and seminars of central banks, all these related directly or indirectly to Big Data in banks

A Framework for understanding & classifying Urban Data Business Models

Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities

From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived

Exploring data conditions to improve business performance

Past researches drew from the industrial organization perspective have examined the role of the data to generate competitive advantage. Their analysis show data is a valuable resource that can leverage business partnerships, vertical integration, or diversification. The emergence of data science has created new opportunities to understand better clients’ needs and to manage more efficiently the organizations’ processes. Nevertheless, if data analytics represent an enormous potential, many organizations are still looking the conditions to obtain value from them. Our study contributes to this topical subject analysing the relationship between different combinations of data conditions and the company performance that we measure through the Customer management and Provider operations efficiency. Our methodology is novel compared to previous researches which are based in linear algebra. It is based on the use of a fuzzy-set qualitative comparative analysis (fsQCA) which allows to reveal multiple paths to achieve the possible outcomes. Our results show that the consistency, completeness, and protection of the data along with a data-driven company profile are different possible solutions to a better Customer management and Provider operations efficiency. Our conclusions allow practitioners to uncover the strength of the data in the hopes of solving many of their business performance concernsPeer ReviewedPostprint (author's final draft