The Monumental Knausgård: Big Data, Quantified Self, and Proust for the Facebook Generation

Narrative ◽  
2018 ◽  
Vol 26 (3) ◽  
pp. 320-338
Author(s):  
Inge van de Ven
Keyword(s):  
Big Data ◽  
Quantified Self

scholarly journals Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Care Providers ◽  
Data Privacy ◽  
Recent Literature ◽  
Unstructured Data ◽  
Small Scale ◽  
Small Data ◽  
Quantified Self ◽  
Care Providers

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

Big Data and Disease Prevention: From Quantified Self to Quantified Communities

Big Data ◽  
2013 ◽  
Vol 1 (3) ◽  
pp. 168-175 ◽  
Author(s):  
Meredith A. Barrett ◽  
Olivier Humblet ◽  
Robert A. Hiatt ◽  
Nancy E. Adler
Keyword(s):  
Big Data ◽  
Disease Prevention ◽  
Quantified Self

Visualising and mapping the intellectual structure of medical big data

2018 ◽  
Vol 45 (2) ◽  
pp. 239-258 ◽  
Author(s):  
Wen-Chin Hsu ◽  
Jia-Huan Li
Keyword(s):  
Big Data ◽  
Intellectual Structure ◽  
Quantified Self ◽  
Research Gaps ◽  
Record System ◽  
Managerial Strategies ◽  
Medical Big Data ◽  
Near Future ◽  
Core Issues ◽  
The Internet Of Things

In this study, we sought to apply recent advances in informetrics to the analysis of literature related to big data in the field of medicine. Our aim was to elucidate research trends, identify knowledge clusters and decipher the links between them. We also sought to ascertain the theories most commonly applied in the processing of medical data and identify potential research gaps. The most important keywords over the last 10 years have been ‘big data’, ‘data mining’, ‘healthcare’, ‘cloud computing’, ‘machine learning’ and ‘electronic health record system’. These could be viewed as the core issues of research associated with big data in the field of medicine. We also identified a number of keywords that are expected to play a pivotal role in this field in the near future. These terms include the ‘internet of things’, ‘e-health’, ‘sensors’, ‘predictive modeling’, ‘quantified self’, ‘smart city’, ‘wearable device’ and ‘m-health’. Finally, we compiled co-word networks indicating the degree of connectivity between keywords, for use in locating knowledge gaps by revealing the overall context of issues commonly encountered when investigating big data. Our findings form a solid academic foundation on which to develop medical technologies, managerial strategies and theory related to big data.

scholarly journals Koncepcja quantified self i jej konsekwencje dla projektowania usług bibliotecznych w świetle literatury przedmiotu

2020 ◽  
Vol 88 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Magdalena Wójcik
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Wearable Computing ◽  
Quantified Self ◽  
Online Library

Teza/cel – Przedmiot artykułu stanowi koncepcja quantified self. Celem jest określenie jej potencjału dla projektowania usług bibliotecznych. Metoda – Zastosowano metodę analizy i krytyki piśmiennictwa. W oparciu o wyszukiwanie prowadzone w katalogu Biblioteki Narodowej i katalogu Worldcat, bazie abstraktowej LISTA, repozytorium ELIS oraz wybranych bazach wielodziedzinowych (np. Science Direct, Wiley Online Library) określono stan badań nad koncepcją quantified self. Pod uwagę wzięto wyłącznie prace opublikowane w języku polskim i angielskim w latach 2010-2018. Wyniki – Omówiono główne założenia koncepcji quantified self, jej związki z przetwarzaniem wielkich danych (ang. big data) oraz miniaturyzacją sprzętu elektronicznego i rozwojem wearable computing, a także związki z koncepcją Internetu Rzeczy (ang. Internet of Things). Określono możliwości wykorzystania założeń podejścia quantified self w projektowaniu usług bibliotecznych. Wnioski – Przeprowadzona analiza pokazuje duży potencjał koncepcji quantified self dla projektowania innowacyjnych usług bibliotecznych, a także dla procesów ich ewaluacji, doskonalenia i promocji.

scholarly journals The Sociology of Quantification: Where Are We Now?

2017 ◽  
Author(s):  
Elizabeth Popp Berman ◽  
Daniel Hirschman
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Google Scholar ◽  
Good Work ◽  
The Internet ◽  
Quantified Self ◽  
Emerging Themes ◽  
The Moment

A decade ago, Wendy Espeland and Mitchell Stevens published an essay titled “The Sociology of Quantification.” In it, they wrote that “sociologists have generally been reluctant to investigate [quantification] as a sociological phenomenon in its own right.” While accountants, anthropologists, and historians had begun the reflexive study of numbers, “sociologists have paid relatively little attention to the spread of quantification or the significance of new regimes of measurement” (Espeland and Stevens 2008:402).That has clearly changed. While Google Scholar shows only nine results for the phrase “sociology of quantification” through 2007, the last decade returns 448. This proliferation of scholarship on numbers goes hand in hand with a proliferation of numbers themselves. New technologies have created a “quantified self,” and the explosion of the internet has produced “big data”. As a senior sociologist recently quipped to one of us, sociology has become quantitative researchers, and qualitative researchers studying quantification. Thus the moment seems ripe for revisiting the sociology of quantification, looking at emerging themes, and seeking signs that a new subfield might be starting to consolidate.Alas, the news is mixed. Lots of good work is being done. The intellectual space is full of ferment. Yet—and perhaps the fact that we were reviewing books by authors from at least four different disciplines should have clued us in earlier—so far, we seem to be looking at a genre, not a subfield....

Big Data, Sport, and the Digital Divide

2016 ◽  
Vol 41 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Andrew Baerg
Keyword(s):  
Big Data ◽  
Digital Divide ◽  
Theoretical Framework ◽  
Data Monitoring ◽  
Quantified Self ◽  
Potential Response ◽  
Starting Point ◽  
The Relationship

This article considers the relationship between Big Data and the athlete. Where Beer and Hutchins have focused on Big Data and sport, this article concentrates on the athlete’s potential response to Big Data monitoring. Drawing on the work of Andrejevic, and Kennedy and Moss, the project speaks to the Big Data–athlete relation through the theoretical framework of the digital divide. It describes Big Data and its relation to the digital divide before tracing out how athletes might respond to Big Data monitoring by presenting concerns about privacy and/or embracing a quantified self. Considering these responses provides a starting point for further work on how athletes should treat Big Data and its implications for sport.

scholarly journals Why data is not enough: Digital traces as control of self and self-control

2014 ◽  
Vol 12 (2) ◽  
pp. 243-254 ◽  
Author(s):  
Tyler Butler Reigeluth
Keyword(s):  
Big Data ◽  
Digital Technology ◽  
Francophone Literature ◽  
The Self ◽  
Self Control ◽  
Quantified Self ◽  
To Come ◽  
New Perspective ◽  
And Control ◽  
Gilbert Simondon

As an alternative to the seemingly natural objectivity and self-evidence of “data,” this paper builds on recent francophone literature by developing a critical conceptualization of “digital traces.” Underlining the materiality and discursiveness of traces allows us to understand and articulate both the technical and sociopolitical implications of digital technology. The philosophies of Gilbert Simondon and Michel Foucault give strong ontological and epistemological groundings for interpreting the relationships between technology and processes of subjectification. In this light, digital traces are framed as objects and products of heteronomous interventions, the logics of which can be traced through the programs and algorithms deployed. Through the empirical examples of “Predictive Policing” and “Quantified Self” digital traces are contrasted with the premises and dreams of Big Data. While the later claims to algorithmically correlative, predict and preempt the future by reducing it to a “what-is-to-come,” the digital trace paradigm offers a new perspective on how forms of self-control and control of the self are interdependent facets of “algorithmic governmentality.”

scholarly journals Integrating Social Scientific Perspectives on the Quantified Employee Self

2019 ◽  
Vol 8 (9) ◽  
pp. 262 ◽  
Author(s):  
Thomas Calvard
Keyword(s):  
Big Data ◽  
Data Science ◽  
Well Being ◽  
Quantified Self ◽  
Sources Of Information ◽  
Organizational Environments ◽  
Theoretical Perspectives ◽  
Power And Control ◽  
Social Scientific ◽  
And Control

A key technological trend in big data science is that of the quantified self, whereby individuals can self-track their health and well-being using various sources of information. The aim of this article was to integrate multidimensional views on the positive and negative implications of the quantified self for employees and workplaces. Relevant human and social scientific literature on the quantified (employee) self and self-tracking were drawn upon and organized into three main influential perspectives. Specifically, the article identified (1) psychological perspectives on quantified attitudes and behaviors, (2) sociological perspectives on sociomaterial user construction, and (3) critical theoretical perspectives on digital power and control. This article suggests that the three perspectives are complementary and can be usefully integrated into an embodied sensemaking perspective. Embodied sensemaking views the employee as a self-conscious user of big data seeking to make sense of their embeddedness in wider digital and organizational environments. This article concludes with implications for protecting employee agency in tension with employers’ big data strategies for governing and managing the performance of quantified digital employee selves.

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