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Open Access 2016 | Open Access | Buch

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New Horizons for a Data-Driven Economy

A Roadmap for Usage and Exploitation of Big Data in Europe

herausgegeben von: José María Cavanillas, Edward Curry, Wolfgang Wahlster

Verlag: Springer International Publishing

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In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy.

The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe.

This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Inhaltsverzeichnis

Frontmatter

The Big Data Opportunity

Frontmatter

Open Access

Chapter 1. The Big Data Value Opportunity
Abstract
Big data is expected to impact all sectors, from healthcare to media, from energy to retail. The ability to effectively manage information and extract knowledge is now seen as a key competitive advantage for organizations. This chapter explores the value potential of big data with a particular focus on the European context. The chapter identifies the positive transformational potential of big data within a number of key sectors and highlights the need for a clear strategy to increase the competitiveness of European industries in order to drive innovation and competitiveness. Europe needs to foster the development and wide adoption of big data technologies, value adding use cases, and sustainable business models through a Big Data Ecosystem. Finally the chapter describes the key dimensions, including skills, legal, business, and social, that need to be addressed in a European Big Data Ecosystem.
José María Cavanillas, Edward Curry, Wolfgang Wahlster

Open Access

Chapter 2. The BIG Project
Abstract
The Big Data Public Private Forum (BIG) Project (http://​www.​big-project.​eu/​) was an EU coordination and support action to provide a roadmap for big data within Europe. The BIG project worked towards the definition and implementation of a clear big data strategy that tackled the necessary activities needed in research and innovation, technology adoption, and the required support from the European Commission necessary for the successful implementation of the big data economy. As part of this strategy, the outcomes of the project were used as input for Horizon 2020.
This chapter provides an overview of the BIG project detailing the project’s mission and strategic objectives. The chapter describes the partners within the consortium and the overall structure of the project work. The three-phase methodology used in the project is described, including details on the techniques used within the technical working groups, sectorial forms, and road mapping activity. Finally, the project’s role in setting up the big data contractual Public Private Partnership (cPPP) and Big Data Value Association is discussed.
Edward Curry, Tilman Becker, Ricard Munné, Nuria De Lama, Sonja Zillner

The Big Data Value Chain: Enabling and Value Creating Technologies

Frontmatter

Open Access

Chapter 3. The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches
Abstract
Big data is the emerging field where innovative technology offers new ways to extract value from the tsunami of available information. As with any emerging area, terms and concepts can be open to different interpretations. The Big Data domain is no different. This chapter examines the different definitions of “Big Data” which have emerged over the last number of years to label data with different attributes. The Big Data Value Chain is introduced to describe the information flow within a big data system as a series of steps needed to generate value and useful insights from data. The value chain enables the analysis of big data technologies for each step within the chain. The chapter explores the concept of a Big Data Ecosystem. It examines the use of the ecosystem metaphor within the business community to describe the business environment and how it can be extended to the big data context. Key stakeholders of a big data ecosystem are identified together with the challenges that need to be overcome to enable a big data ecosystem in Europe.
Edward Curry

Open Access

Chapter 4. Big Data Acquisition
Abstract
Different data processing architectures for big data have been proposed to address the different characteristics of big data. Data acquisition has been understood as the process of gathering, filtering, and cleaning data before the data is put in a data warehouse or any other storage solution. The acquisition of big data is most commonly governed by four of the Vs: volume, velocity, variety, and value. Most data acquisition scenarios assume high-volume, high-velocity, high-variety, but low-value data, making it important to have adaptable and time-efficient gathering, filtering, and cleaning algorithms that ensure that only the high-value fragments of the data are actually processed by the data-warehouse analysis. The goals of this chapter are threefold: First, it aims to identify the current requirements for data acquisition by presenting open state-of-the-art frameworks and protocols for big data acquisition for companies. The second goal is to unveil the current approaches used for data acquisition in the different sectors. Finally, it discusses how the requirements of data acquisition are met by current approaches as well as possible future developments in the same area.
Klaus Lyko, Marcus Nitzschke, Axel-Cyrille Ngonga Ngomo

Open Access

Chapter 5. Big Data Analysis
Abstract
The value of big data is predicated on the ability to detect trends and patterns and more generally to make sense of the large volumes of data that is often comprised of a heterogeneous mix of format, structure, and semantics. Big data analysis is the component of the big data value chain that focuses on transforming raw acquired data into a coherent usable resource suitable for analysis. Using a range of interviews with key stakeholders in small and large companies and academia, this chapter outlines key insights, state of the art, emerging trends, future requirements, and sectorial case studies for data analysis.
John Domingue, Nelia Lasierra, Anna Fensel, Tim van Kasteren, Martin Strohbach, Andreas Thalhammer

Open Access

Chapter 6. Big Data Curation
Abstract
With the emergence of data environments with growing data variety and volume, organizations need to be supported by processes and technologies that allow them to produce and maintain high-quality data facilitating data reuse, accessibility, and analysis. In contemporary data management environments, data curation infrastructures have a key role in addressing the common challenges found across many different data production and consumption environments. Recent changes in the scale of the data landscape bring major changes and new demands to data curation processes and technologies. This chapter investigates how the emerging big data landscape is defining new requirements for data curation infrastructures and how curation infrastructures are evolving to meet these challenges. Different dimensions of scaling-up data curation for big data are described, including emerging technologies, economic models, incentive models, social aspects, and supporting standards. This analysis is grounded by literature research, interviews with domain experts, surveys, and case studies and provides an overview of the state-of-the-art, future requirements and emerging trends in the field.
André Freitas, Edward Curry

Open Access

Chapter 7. Big Data Storage
Abstract
This chapter provides an overview of big data storage technologies. It is the result of a survey of the current state of the art in data storage technologies in order to create a cross-sectorial technology roadmap. This chapter provides a concise overview of big data storage systems that are capable of dealing with high velocity, high volumes, and high varieties of data. It describes distributed file systems, NoSQL databases, graph databases, and NewSQL databases. The chapter investigates the challenge of storing data in a secure and privacy-preserving way. The social and economic impact of big data storage technologies is described, open research challenges highlighted, and three selected case studies are provided from the health, finance, and energy sector. Some of the key insights on big data storage are (1) in-memory databases and columnar databases typically outperform traditional relational database systems, (2) the major technical barrier to widespread up-take of big data storage solutions are missing standards, and (3) there is a need to address open research challenges related to the scalability and performance of graph databases.
Martin Strohbach, Jörg Daubert, Herman Ravkin, Mario Lischka

Open Access

Chapter 8. Big Data Usage
Abstract
Big data usage covers the business goals that need access to data, its analyses, and integration into business decision-making. This chapter gives an overview of the applications of big data, focusing on decision support through big data in different sectors. Big data usage is a wide field that is addressed in this chapter by viewing data usage from various perspectives, including the underlying technology stacks, trends in various sectors, the impact on business models, and requirements on human–computer interaction. The chapter explores data usage tools, query and scripting languages, execution engines, APIs, programming models, different technology stacks, and some of the trade-offs involved are discussed. The chapter presents general aspects of decision support, followed by a discussion of specific access to analysis results through visualization and new explorative interfaces. Emerging trends and future requirements are presented with special emphasis on Industry 4.0 and the emerging need for smart data and smart services.
Tilman Becker

Usage and Exploitation of Big Data

Frontmatter

Open Access

Chapter 9. Big Data-Driven Innovation in Industrial Sectors
Abstract
This chapter provides the conceptual background and overview of big data-driven innovation in society. Specifically, it examines the nature of data-driven innovation, exemplars of big data-driven innovations in sectors spanning healthcare, public sector, finance, media, energy, and transport. It discusses core enablers for these innovations highlighting factors and challenges associated with the adequate diffusion, uptake, and sustainability of big data-driven initiatives. Finally, it presents policy recommendations to guide the development of a big data innovation ecosystem.
Sonja Zillner, Tilman Becker, Ricard Munné, Kazim Hussain, Sebnem Rusitschka, Helen Lippell, Edward Curry, Adegboyega Ojo

Open Access

Chapter 10. Big Data in the Health Sector
Abstract
Several developments in the healthcare sector, such as escalating healthcare costs, increased need for healthcare coverage, and shifts in provider reimbursement trends, trigger the demand for big data technology. The wide scope and variety of discussed big data applications indicate the promising opportunities of big data technologies to improve overall healthcare delivery. However, in order to realize those applications, one needs to enable seamless access to the various health data sets. As of today, access to health data is only possible in a very constrained and limited manner. In order to improve this situation and for establishing the basis for the widespread implementation of big data applications in the healthcare sector, several technical requirements such as the semantic enrichment of data, data integration and sharing, data privacy and security, as well as data quality, need to be addressed. In terms of market adoption, the big data revolution in the healthcare domain is in a very early stage with the most potential for value creation and business development unclaimed as well as unexplored. Current roadblocks are the established system incentives of the healthcare system, which hinder collaboration and, thus, data sharing and exchange. The trend towards value-based healthcare delivery will foster the collaboration of the stakeholder to enhance the value of the patient’s treatment, and thus will significantly foster the need for big data applications.
Sonja Zillner, Sabrina Neururer

Open Access

Chapter 11. Big Data in the Public Sector
Abstract
The public sector is becoming increasingly aware of the potential value to be gained from big data, as governments generate and collect vast quantities of data through their everyday activities.
The benefits of big data in the public sector can be grouped into three major areas, based on a classification of the types of benefits: advanced analytics, through automated algorithms; improvements in effectiveness, providing greater internal transparency; improvements in efficiency, where better services can be provided based on the personalization of services; and learning from the performance of such services.
The chapter examined several drivers and constraints that have been identified, which can boost or stop the development of big data in the sector depending on how they are addressed. The findings, after analysing the requirements and the technologies currently available, show that there are open research questions to be addressed in order to develop such technologies so competitive and effective solutions can be built. The main developments are required in the fields of scalability of data analysis, pattern discovery, and real-time applications. Also required are improvements in provenance for the sharing and integration of data from the public sector. It is also extremely important to provide integrated security and privacy mechanisms in big data applications, as public sector collects vast amounts of sensitive data. Finally, respecting the privacy of citizens is a mandatory obligation in the European Union.
Ricard Munné

Open Access

Chapter 12. Big Data in the Finance and Insurance Sectors
Abstract
The finance and insurance sector by nature has been an intensively data-driven industry, managing large quantities of customer data and with areas such as capital market trading having used data analytics for some time.
The advent of big data in financial services can bring numerous advantages to financial institutions: enhanced levels of customer insight, engagement, and experience through the digitization of financial products and services and with the increasing trend of customers interacting with brands or organizations in the digital space; enhanced fraud detection and prevention capabilities through the use of big data it is now possible to use larger datasets to identify trends that indicate fraud; and enhanced market trading analysis, where trading strategies which make the use of sophisticated computer algorithms to rapidly trade the financial markets.
This chapter identifies the drivers related with the evolution of the sector, like the impact of regulations, and changing business models, together with the associated constraints related with legacy culture and infrastructures, and data privacy and security issues. The findings, after analysing the requirements and the technologies currently available, show that there are still research challenges to develop the technologies to their full potential in order to provide competitive and effective solutions. These challenges appear at all levels of the big data chain and involve a wide set of different technologies, which would make necessary a prioritization of the investments in R&D, for example, real-time aspects, better data quality techniques, scalability of data management and processing, and better sentiment classification methods.
Kazim Hussain, Elsa Prieto

Open Access

Chapter 13. Big Data in the Energy and Transport Sectors
Abstract
Massive amounts of sensor and textual data await the energy and transport sector stakeholders once the digital transformation of the sector reaches its tipping point. This chapter gives a definition of big data application scenarios through examples in different segments of the energy and transport sectors. A mere utilization of existing big data technologies as employed by online businesses will not be sufficient. Domain-specific big data technologies are needed for cyber-physical energy and transport systems, while the focus needs to move beyond big data to smart data technologies. Unless the need for privacy and confidentiality is satisfied, there will always be regulatory uncertainty and barriers to user acceptance of new data-driven offerings. The chapter concludes with recommendations that will help sustain the quality and competitiveness of European infrastructures as it undergoes a digital transformation.
Sebnem Rusitschka, Edward Curry

Open Access

Chapter 14. Big Data in the Media and Entertainment Sectors
Abstract
The media and entertainment industries are evolving at an unprecedented rate, driven by the twin needs to reduce operating costs and simultaneously generate more revenue from increasingly competitive and uncertain markets. Media companies are in many respects an early adopter of big data technologies because it enables them to drive digital transformation, exploiting more fully not only data which was already available, but also new sources of data from both inside and outside the organization. This chapter presents a wide-ranging overview of the state of the art of big data in the media sector. It introduces the industrial needs, application scenarios, and other aspects of the sector and describes how they influence, and are influenced by, products, customers, and processes. Finally, the research is distilled into a comprehensive set of requirements across the entire big data value chain, alongside the consolidated roadmap tracking the development of key technologies to support semantic data enrichment, data quality, data-driven innovation, and data analysis.
Helen Lippell

A Roadmap for Big Data Research

Frontmatter

Open Access

Chapter 15. Cross-sectorial Requirements Analysis for Big Data Research
Abstract
This chapter identifies the cross-sectorial requirements for big data research necessary to define a research roadmap. The aim of the roadmap is to maximize and sustain the impact of big data technologies and applications in different industrial sectors by identifying and driving opportunities in Europe. This chapter details the process used to consolidate the big data requirements from different sectors into a single roadmap. The results comprise a prioritized set of cross-sector requirements that were used to define the technology policy, business, and society roadmaps together with action recommendations. This chapter presents a summarized description of the cross-sectorial consolidated requirements. It discusses each of the high-level and sub-level requirements together with the associated challenges that need to be tackled. Finally, the chapter concludes with a prioritization of the cross-sectorial requirements based on their expected impacts.
Tilman Becker, Edward Curry, Anja Jentzsch, Walter Palmetshofer

Open Access

Chapter 16. New Horizons for a Data-Driven Economy: Roadmaps and Action Plans for Technology, Businesses, Policy, and Society
Abstract
This chapter describes big data roadmaps for Europe in the areas of technology, business, policy, and society. The roadmaps outline the most urgent and challenging issues for big data in Europe. They are the result of over 2 years of extensive research and input from a wide range of stakeholders from the European big data ecosystem. The roadmaps will foster the creation of a more stable big data environment by enabling enterprises, business, entrepreneurs, SMEs, and society to gain from the benefits of big data in Europe. The chapter introduces the Big Data Value Association (BDVA) and the Big Data Value contractual Public Private Partnership (BDV cPPP) and describes the role played by the BIG project in their establishment. The BDVA and the BDV cPPP will provide the necessary framework for industrial leadership, investment, and commitment of both the private and public side to build a data-driven economy across Europe.
Tilman Becker, Edward Curry, Anja Jentzsch, Walter Palmetshofer
Backmatter
Metadaten
Titel
New Horizons for a Data-Driven Economy
herausgegeben von
José María Cavanillas
Edward Curry
Wolfgang Wahlster
Copyright-Jahr
2016
Electronic ISBN
978-3-319-21569-3
Print ISBN
978-3-319-21568-6
DOI
https://doi.org/10.1007/978-3-319-21569-3