scholarly journals Big Data: las implicancias de la democratización de los métodos. Oportunidades y desafíos que los dispositivos y datos digitales plantean para la investigación social

2016 ◽  
pp. 237
Author(s):  
Andrés Asenjo Morosetti ◽  
Christian Riveros Pino
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Research ◽  
Social Practices ◽  
Digital Data ◽  
Technology Research ◽  
Social Production ◽  
Social Scientists ◽  
Challenges And Opportunities ◽  
The Subject

ResumenEl presente ensayo monográfico intenta brindar una panorámica globaldel fenómeno del Big Data, entendido como un sistema de producción,procesamiento, sistematización y análisis de datos sociales a gran escala.Actualmente, una multiplicidad de entes se han posicionado como agentesrecopiladores de datos sobre conductas y prácticas sociales, gracias aavances tecnológicos que han incrementado las capacidades sistémicas parala obtención y análisis de grandes volúmenes de información. Esto suponeuna suerte de democratización de los métodos, lo cual plantea una serie dedesafíos y oportunidades para los cientistas sociales, otrora agentes exclusivosde la producción de lo social. Ante esto, el presente documento ofrece unmarco general de análisis, esbozando reflexiones temáticas y estratégicaspara la comunidad científica en general y para la sociología en particular. Elensayo se estructura a partir de tres secciones: una introducción, que ofreceun acercamiento global al tema; una sección de discusión, en la que se revisaa grandes rasgos las principales problemáticas y desafíos que presenta elBig Data para la investigación social y los científicos sociales, y una secciónde conclusiones, en la que se esbozan posibles alternativas de integraciónmetodológica entre las nuevas formas de investigación social y los métodostradicionales.Palabras clave: Big Data, tecnologías de la información, métodos deinvestigación, investigación social, empirismo.Big Data: Implications of the democratization ofmethods.Opportunities and challenges for social researchprovided by digital data and devicesAbstractThis monographic essay attempts to provide a comprehensive overviewof the phenomenon of Big Data, understood as a system of production,processing, systematization and analysis of large-scale social data. Currently,a multiplicity of entities have positioned themselves as agents collectingdata, behaviors and social practices, thanks to technological advancesthat have increased systemic capacities for collection and analysis of largevolumes of information. This is a sort of democratization of methods, whichposes a number of challenges and opportunities for social scientists, onceexclusive agents of social production. In this way, the present documentprovides a general framework for analysis outlining thematic and strategicconsiderations for the scientific community in general and sociology inparticular. The essay is structured in three sections: an introduction, whichprovides a comprehensive approach to the subject; a section of discussion,which reviews in broad terms the main problems and challenges imposed byBig Data for social research and social scientists; and a section of conclusionswith possible alternatives for methodological integration among new formsof social research and the traditional methods.Keywords: Big Data, information technology, research methods, socialresearch, empiricism.Big Data: as implicações da democratização dosmétodos.Oportunidades e desafios que os dispositivos edados digitais projetam para a pesquisa socialResumoO presente ensaio monográfico tenta oferecer uma visão panorâmicado fenómeno do Big Data, entendido como um sistema de produção,processamento, sistematização e análise dos dados sociais em grandeescala. Atualmente, uma multiplicidade de entidades têm se posicionadocomo agentes coletores de dados sobre comportamentos e práticas sociais,graças aos avanços tecnológicos que aumentaram as capacidades sistêmicaspara a obtenção e análise de grandes volumes de informação. Esta é umaespécie de democratização dos métodos, o que coloca uma série de desafiose oportunidades para os cientistas sociais, pois em outros tempos foram osagentes exclusivos de produção social. Diante disso, o presente documentoapresenta um quadro geral de análise, delineando reflexões temáticase estratégicas para a comunidade científica em geral e para a sociologiaem particular. O ensaio é estruturado em três seções: uma introdução,que oferece uma abordagem global para o tema; uma seção de discussão, onde é revistada em termos gerais, os principais problemas e desafios queapresenta a Big Data para a investigação social e os cientistas sociais, e umaseção de conclusões onde se esboçam possíveis alternativas de integraçãometodológica entre as novas formas de investigação social e os métodostradicionais.Palavras-chave: Big Data, tecnologias da informação, métodos de pesquisa,pesquisa social, empirismo.

scholarly journals Big Data: las implicancias de la democratización de los métodos. Oportunidades y desafíos que los dispositivos y datos digitales plantean para la investigación social

2016 ◽  
pp. 237
Author(s):  
Andrés Asenjo Morosetti ◽  
Christian Riveros Pino
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Research ◽  
Social Practices ◽  
Digital Data ◽  
Technology Research ◽  
Social Production ◽  
Social Scientists ◽  
Challenges And Opportunities ◽  
The Subject

ResumenEl presente ensayo monográfico intenta brindar una panorámica globaldel fenómeno del Big Data, entendido como un sistema de producción,procesamiento, sistematización y análisis de datos sociales a gran escala.Actualmente, una multiplicidad de entes se han posicionado como agentesrecopiladores de datos sobre conductas y prácticas sociales, gracias aavances tecnológicos que han incrementado las capacidades sistémicas parala obtención y análisis de grandes volúmenes de información. Esto suponeuna suerte de democratización de los métodos, lo cual plantea una serie dedesafíos y oportunidades para los cientistas sociales, otrora agentes exclusivosde la producción de lo social. Ante esto, el presente documento ofrece unmarco general de análisis, esbozando reflexiones temáticas y estratégicaspara la comunidad científica en general y para la sociología en particular. Elensayo se estructura a partir de tres secciones: una introducción, que ofreceun acercamiento global al tema; una sección de discusión, en la que se revisaa grandes rasgos las principales problemáticas y desafíos que presenta elBig Data para la investigación social y los científicos sociales, y una secciónde conclusiones, en la que se esbozan posibles alternativas de integraciónmetodológica entre las nuevas formas de investigación social y los métodostradicionales.Palabras clave: Big Data, tecnologías de la información, métodos deinvestigación, investigación social, empirismo.Big Data: Implications of the democratization ofmethods.Opportunities and challenges for social researchprovided by digital data and devicesAbstractThis monographic essay attempts to provide a comprehensive overviewof the phenomenon of Big Data, understood as a system of production,processing, systematization and analysis of large-scale social data. Currently,a multiplicity of entities have positioned themselves as agents collectingdata, behaviors and social practices, thanks to technological advancesthat have increased systemic capacities for collection and analysis of largevolumes of information. This is a sort of democratization of methods, whichposes a number of challenges and opportunities for social scientists, onceexclusive agents of social production. In this way, the present documentprovides a general framework for analysis outlining thematic and strategicconsiderations for the scientific community in general and sociology inparticular. The essay is structured in three sections: an introduction, whichprovides a comprehensive approach to the subject; a section of discussion,which reviews in broad terms the main problems and challenges imposed byBig Data for social research and social scientists; and a section of conclusionswith possible alternatives for methodological integration among new formsof social research and the traditional methods.Keywords: Big Data, information technology, research methods, socialresearch, empiricism.Big Data: as implicações da democratização dosmétodos.Oportunidades e desafios que os dispositivos edados digitais projetam para a pesquisa socialResumoO presente ensaio monográfico tenta oferecer uma visão panorâmicado fenómeno do Big Data, entendido como um sistema de produção,processamento, sistematização e análise dos dados sociais em grandeescala. Atualmente, uma multiplicidade de entidades têm se posicionadocomo agentes coletores de dados sobre comportamentos e práticas sociais,graças aos avanços tecnológicos que aumentaram as capacidades sistêmicaspara a obtenção e análise de grandes volumes de informação. Esta é umaespécie de democratização dos métodos, o que coloca uma série de desafiose oportunidades para os cientistas sociais, pois em outros tempos foram osagentes exclusivos de produção social. Diante disso, o presente documentoapresenta um quadro geral de análise, delineando reflexões temáticase estratégicas para a comunidade científica em geral e para a sociologiaem particular. O ensaio é estruturado em três seções: uma introdução,que oferece uma abordagem global para o tema; uma seção de discussão, onde é revistada em termos gerais, os principais problemas e desafios queapresenta a Big Data para a investigação social e os cientistas sociais, e umaseção de conclusões onde se esboçam possíveis alternativas de integraçãometodológica entre as novas formas de investigação social e os métodostradicionais.Palavras-chave: Big Data, tecnologias da informação, métodos de pesquisa,pesquisa social, empirismo.

scholarly journals Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171882381 ◽  
Author(s):  
Lucy Resnyansky
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Task Demands ◽  
Digital Data ◽  
Conceptual Frameworks ◽  
Social Scientists ◽  
The Social ◽  
Epistemological Challenge ◽  
Social Scientific

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

Kinds of freshwater and their relation to ecosystem services and human well-being

Water Policy ◽  
2016 ◽  
Vol 18 (5) ◽  
pp. 1229-1246 ◽  
Author(s):  
Shakeel Hayat ◽  
Joyeeta Gupta
Keyword(s):  
Ecosystem Services ◽  
Large Scale ◽  
Multiple Scales ◽  
Water Governance ◽  
Well Being ◽  
Ecological Systems ◽  
Social Needs ◽  
Social Scientists ◽  
Social Ecological Systems ◽  
The Subject

The concept of ecosystem services (ESS) has evolved as a link between society and the environment and is recognized by both natural and social scientists. While this concept is increasingly being developed and applied to various ecological systems, it has not been defined specifically for different kinds of water. As water circulation is crucial for large-scale services, such as climatic and hydrological regulation at global and regional scales, water provides specific ecological and anthropogenic services needed for a myriad of chemical, biological, and social needs. Various scholars mostly deal with one specific kind of water, while efforts at water governance need to understand that water passes through different phases and geographical locations providing different services at multiple scales within social-ecological systems. Hence, this paper addresses the question: what are the ESS of different kinds of freshwater and how are these services linked to human well-being? This paper investigates the literature on the subject to create a taxonomy of the kinds of water and their relations to ESS and human well-being. The paper concludes by identifying the implications for governing different kinds of water in order to enhance the potential for optimizing the ESS provided by water in its different phases.

An Enhancement of Cloud Based Sentiment Analysis and BDAAs Using SVM Based Lexicon Dictionary and Adaptive Resource Scheduling

2018 ◽  
Vol 15 (2) ◽  
pp. 437-445 ◽  
Author(s):  
S. Radha ◽  
C. Nelson Kennedy Babu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Resource Scheduling ◽  
Data Integrity ◽  
Digital Data ◽  
Large Set ◽  
Compression Technique ◽  
Adaptive Resource

At present, the cloud computing is emerging technology to run the large set of data capably, and due to fast data growth, processing of large scale data is becoming a main point of information method and customers can estimate the quality of brands of products employing the information given by new digital marketing channels in social media. Thus, every enterprise requires finding and analyzing a big amount of digital data in order to develop their reputation among the customers. Therefore, in this paper, SLA (Service Level Agreement) based BDAAs (Big Data Analytic Applications) using Adaptive Resource Scheduling and big data with cloud based sentiment analysis is proposed to provide the deep web mining, QoS and to analyze the customer behaviors about the product. In this process, the spatio-temporal compression technique can be applied to data compression for reduction of big data. The data is classified in to positive, negative or neutral by employing the SVM with lexicon dictionary based on the customers' behaviors about brand or products. In cloud computing environment, complex to the reduction of resources cost and fluctuation of resource requirements with BDAAs. As a result, it is needed to have a common Analytics as a Service (AaaS) platform that provides a BDAAs to customers in different fields as unpreserved services in a simple to utilize a way with lower cost. Therefore, SLA based BDAAs is developed to utilize the adaptive resource scheduling depending on the customer behaviors and it can provide visualization and data integrity. Our method can give privacy of cloud owner's information with help of data integrity and authentication process. Experimental results of proposed system shows that the sentiment analysis method for online product using cloud based big data is able to classify the opinions of customers accurately and effective of the algorithm in guarantee of SLA.

scholarly journals Digital Transformation, Big Data and Research Landscape in Digital Communication

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Imam Syafganti
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Research Method ◽  
Social Research ◽  
Digital Data ◽  
Communication Research ◽  
Research Directions ◽  
Critical Issues ◽  
Data Source ◽  
Media Research

The digitization of communications technology has led to an intense interaction between human and digital-based technology. A large number of digital data traces produced by humans as a result of that activity. Such data is commonly referred to Big Data. The availability of Big Data as a digital data source in turn, opens opportunities for communication scientists to be able to use that data to get the patterns and trends of human activities that have been done through social research. It is necessary to understand the basic concept of the Big Data, using appropriate tools and adequate access to the data, and appropriate research method in order to be able to conduct research by using such digital data. This paper aims to describe the potential of Big Data for the purposes of communication research, the use of appropriate tools, techniques and methods and to identify potential research directions in the digital realm. Some limitations and critical issues related to the research validity, population and sample, as well as ethics in digital media research method were also discussed.

scholarly journals Big Data and Human Geography

2021 ◽  
Author(s):  
Francisco Rowe
Keyword(s):  
Big Data ◽  
Human Geography ◽  
Digital Data ◽  
Ethical Challenges ◽  
Human Interactions ◽  
Technological Advances ◽  
Extensive Coverage ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
And Storage

Technological advances have enabled the emerge of ‘Big Data’ through the production, processing, analysis and storage of large volumes of digital data. Data that could not previously be stored or used to be captured using analog devices can now be digitally recorded. This chapter identifies and discusses the existing and future challenges and opportunities of Big Data for human geography. Big Data offer high geographic and temporal granularity, extensive coverage and instant information to transform our understanding of human interactions and our social world. At the same time, Big Data present major epistemological, methodological and ethical challenges which need to be addressed to realise these opportunities. I identify the key challenges and actions for the future of human geography emerging from the use of Big Data.

scholarly journals Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo

2017 ◽  
Author(s):  
Michael J Madison
Keyword(s):  
Big Data ◽  
Citizen Science ◽  
Large Scale ◽  
Data Science ◽  
Digital Data ◽  
Peer Production ◽  
Data Intensive ◽  
Knowledge Commons ◽  
Large Numbers ◽  
Citizen Science Project

The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, sometimes known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.

scholarly journals Caring for (Big) Data: An Introduction to Research Methodologies and Ethical Challenges in Digital Migration Studies

2021 ◽  
pp. 1-21
Author(s):  
Marie Sandberg ◽  
Luca Rossi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Activity ◽  
Data Access ◽  
Digital Data ◽  
Data Sets ◽  
Ethical Challenges ◽  
Migration Studies ◽  
Critical Approach ◽  
Migration Research

AbstractDigital technologies present new methodological and ethical challenges for migration studies: from ensuring data access in ethically viable ways to privacy protection, ensuring autonomy, and security of research participants. This Introductory chapter argues that the growing field of digital migration research requires new modes of caring for (big) data. Besides from methodological and ethical reflexivity such care work implies the establishing of analytically sustainable and viable environments for the respective data sets—from large-scale data sets (“big data”) to ethnographic materials. Further, it is argued that approaching migrants’ digital data “with care” means pursuing a critical approach to the use of big data in migration research where the data is not an unquestionable proxy for social activity but rather a complex construct of which the underlying social practices (and vulnerabilities) need to be fully understood. Finally, it is presented how the contributions of this book offer an in-depth analysis of the most crucial methodological and ethical challenges in digital migration studies and reflect on ways to move this field forward.

Big data and extreme-scale computing

2018 ◽  
Vol 32 (4) ◽  
pp. 435-479 ◽  
Author(s):  
M Asch ◽  
T Moore ◽  
R Badia ◽  
M Beck ◽  
P Beckman ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance ◽  
Large Scale ◽  
Digital Data ◽  
Distributed Sensors ◽  
Large Scale Data ◽  
Critical Problems ◽  
Extreme Scale ◽  
Shared Infrastructure ◽  
Large Scale Simulations

Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.

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