Framing Big Data

Framing Big Data

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  • Author: Maria Cristina Paganoni
  • Publisher: Springer
  • ISBN: 3030167887
  • Category : Language Arts & Disciplines
  • Languages : en
  • Pages : 116

This book addresses big data as a socio-technical construct with huge potential for innovation in key sectors such as healthcare, government and business. Big data and its increasingly widespread use in such influential spheres can generate ethically controversial decisions, including questions surrounding privacy, consent and accountability. This book attempts to unpack the epistemological implications of the term ‘big data’, as well as the opportunities and responsibilities which come with it. The author analyses the linguistic texture of the big data narrative in the news media, in healthcare and in EU law on data protection, in order to contribute to its understanding from the critical perspective of language studies. The result is a study which will be of interest to students and scholars working in the digital humanities, corpus linguistics, and discourse studies.


The Politics and Policies of Big Data

The Politics and Policies of Big Data

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  • Author: Ann Rudinow Sætnan
  • Publisher: Routledge
  • ISBN: 1351866540
  • Category : Social Science
  • Languages : en
  • Pages : 358

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.


Big Data Research for Social Sciences and Social Impact

Big Data Research for Social Sciences and Social Impact

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  • Author: Miltiadis D. Lytras
  • Publisher: MDPI
  • ISBN: 3039282204
  • Category : Technology & Engineering
  • Languages : en
  • Pages : 416

A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.


Roundtable on Data Science Postsecondary Education

Roundtable on Data Science Postsecondary Education

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  • Author: National Academies of Sciences, Engineering, and Medicine
  • Publisher: National Academies Press
  • ISBN: 0309677734
  • Category : Education
  • Languages : en
  • Pages : 223

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.


Big Data

Big Data

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  • Author: Wolfgang Pietsch
  • Publisher: Cambridge University Press
  • ISBN: 110860448X
  • Category : Science
  • Languages : en
  • Pages : 142

Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.


Big Data?

Big Data?

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  • Author: Martin Hand
  • Publisher: Emerald Group Publishing
  • ISBN: 1784410500
  • Category : Social Science
  • Languages : en
  • Pages : 250

This book examines and engages with the ambivalence of digitization, illuminating the diverse ways in which researchers approach, negotiate, understand and interpret objects and practices of digital research.


Big Data and Ethics

Big Data and Ethics

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  • Author: Jérôme Béranger
  • Publisher: Elsevier
  • ISBN: 0081010621
  • Category : Medical
  • Languages : en
  • Pages : 324

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this “massive data seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data


Cybersecurity Data Science

Cybersecurity Data Science

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  • Author: Scott Mongeau
  • Publisher: Springer Nature
  • ISBN: 3030748960
  • Category : Computers
  • Languages : en
  • Pages : 410

This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.


Big Data Analytics

Big Data Analytics

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  • Author: David Loshin
  • Publisher: Elsevier
  • ISBN: 0124186645
  • Category : Computers
  • Languages : en
  • Pages : 142

Big Data Analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise. Guides the reader in assessing the opportunities and value proposition Overview of big data hardware and software architectures Presents a variety of technologies and how they fit into the big data ecosystem


Thick Big Data

Thick Big Data

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  • Author: Dariusz Jemielniak
  • Publisher: Oxford University Press, USA
  • ISBN: 0198839707
  • Category : Business & Economics
  • Languages : en
  • Pages : 208

The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.