Big Data Practices Research Articles

Analysis Method of Motion Information Driven by Medical Big Data

Nowadays, with the maturity and development of Internet, cloud computing and Internet of things technology, and the wide popularization of medical / health information technology, medical / health related data is growing at an amazing speed. At the same time, the popularization and application of genomic technology and the rapid development of wearable mobile medical and mobile health technology, promote the field of health care to enter the era of big data. Traditional sports risk assessment methods can assess the risks in sports, but there are problems such as time-consuming and poor accuracy in the assessment process, which can not be used in large-scale sports assessment. A method of motion risk assessment based on big data analysis is proposed. Based on the analysis of risk factors, this paper constructs a risk assessment model of large-scale sports, and introduces multi-level superposition operation and multi-factor mediation variance method, processes the sports risk data, and realizes the sports risk assessment using BP neural network based on the big data analysis method. The experimental results show that the proposed method based on big data analysis, compared with the traditional risk assessment method, can carry out high-efficiency and accurate motion risk assessment, and can be applied to large-scale risk assessment. This system has theoretical and practical significance for guiding the theory and practice of big data driven health / disease management, promoting the transformation and upgrading of sports health management and the development of health care big data industrialization.

R3D3: A Doubly Opportunistic Data Structure for Compressing and Indexing Massive Data

Opportunistic data structures are used extensively in big data practice to break down the massive storage space requirements of processing large volumes of information. A data structure is called (singly) opportunistic if it takes advantage of the redundancy in the input in order to store it in iformationtheoretically minimum space. Yet, efficient data processing requires a separate index alongside the data, whose size often substantially exceeds that of the compressed information. In this paper, we introduce doubly opportunistic data structures to not only attain best possible compression on the input data but also on the index. We present R3D3 that encodes a bitvector of length n and Shannon entropy H0 to nH0 bits and the accompanying index to nH0(1/2 + O(log C/C)) bits, thus attaining provably minimum space (up to small error terms) on both the data and the index, and supports a rich set of queries to arbitrary position in the compressed bitvector in O(C) time when C = o(log n). Our R3D3 prototype attains several times space reduction beyond known compression techniques on a wide range of synthetic and real data sets, while it supports operations on the compressed data at comparable speed.

Big Data Ethics and Politics: Toward New Understandings

The hype around big data does not seem to abate nor do the scandals. Privacy breaches in the collection, use, and sharing of big data have affected all the major tech players, be it Facebook, Google, Apple, or Uber, and go beyond the corporate world including governments, municipalities, and educational and health institutions. What has come to light is that enabled by the rapid growth of social media and mobile apps, various stakeholders collect and use large amounts of data, disregarding the ethics and politics. As big data touch on many realms of daily life and have profound impacts in the social world, the scrutiny around big data practice becomes increasingly relevant. This special issue investigates the ethics and politics of big data using a wide range of theoretical and methodological approaches. Together, the articles provide new understandings of the many dimensions of big data ethics and politics, showing it is important to understand and increase awareness of the biases and limitations inherent in big data analysis and practices.

A Culture of Competition

This article considers the contribution of sports to the emergence of a contemporary big data culture. Why and how did media sports become so entangled with big data? How do media sports impact on the popularisation of big data as a cultural practice and as a cultural imaginary? In the first part of the article, I demonstrate how, as early as the nineteenth century, the standardisation and serialisation of sport competitions went hand in hand with a growing relevance of quantified evaluation of performances. Sports contributed to the modern ‘avalanche of numbers’ and thus became an important symbolic resource for the broader implementation of a data-based, ‘normalistic’ regulation of social practices. In the second part, I focus on the implementation of a public representation of actual big data practices in professional sports. Starting with a short overview of an initially slow-moving, but eventually comprehensive, appropriation of advanced statistical calculations and other big data practices since the 1970s, I analyse examples that illustrate the controversies around and the public legitimisation of metrics and data visualisation. My main claim is that sport, because of its historically long and close entanglement with numbers, both stimulates a naturalisation of datafied competition and fuels an ongoing debate about the quality and implications of different forms of metrics.

Big Data, Big Brother, Big Profit?

Chinese social media and big data represent an important share of the global Internet, but have received relatively less attention. This editorial examines three dominant discourses based on China’s distinctive and complex political, economic and social realities: “Big Data” (technical focus), “Big Brother” (political focus), and “Big Profit” (economic focus). We argue that the prevailing discourse and practice of big data in China is largely technocentric, decontextualized and nonreflexive, and much less attuned to the social, political, cultural, epistemological, and ethical implications of big data that a human-centric approach would demand. Second, the authoritarian Chinese state poses incredible political challenges to big data research and practice. Third, the practice of Chinese social media and big data is imbued with a discourse of technological nationalism, driven by a handful of monopolistic “national champions.” Despite contention, the state and market players have formed a largely mutually beneficial symbiotic relationship to maximize their political and economic gains. We argue a comparative perspective to foster a global conversation on social media and big data is necessary in order to formulate collective responses to such challenges.

A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid

With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. The Smart Grid (SG) built on top of IoT (Internet of Things) is one of the foundations of this CPS revolution, which involves a large number of smart objects connected by networks. The volume of time series of SG equipment is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing a tremendous volume of raw time series thereby providing a solid support for precise time series analytics now becomes tricky. In this article, we propose a dependable time series analytics (DTSA) framework for IoT-based SG. Our proposed DTSA framework is capable of providing a dependable data transforming from CPS to the target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-network-regularization-based matrix factorization method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online lightweight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.

Democratic governance in an age of datafication: Lessons from mapping government discourses and practices

There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ counter-mapping to map government big data practices and internal discussions of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention.

Big Data in Marketing Arena. Big Opportunity, Big Challenge, and Research Trends: An Integrated View

Big Data has earned much attention from the IT industry and academia. Given the research interest on Big Data in the marketing field, thus, authors intend to present an integrated view based on the previous literature with the aim of identifying the major trends in this field. Particularly, the analysis focus on pertinent terms and topics related to three B's: Big Data, Big Opportunities/benefits, Big challenges. A systematic integrated literature review was carried out through a strict search approach that identified. A total of 650 articles published between 2010 and 2018 indexed in EBSCO database were collected and scrutinized. The results of the previous studies on big data and marketing revealed that they were limited to discuss the technical and research fields and did not clearly consistent with sophisticated technologies towards the benefits of marketing, and it has not yet reached the level of marketers' ambition. Over and above, findings show that research in big data applications and practices to marketing is still in early stages, thus, making it necessary to promote more direct efforts towards the business for big data to thrive in the marketing domain. This paper is specifically relevant to marketing firms and departments concerned in the evolution of marketing activities and abilities to increase customer basis. Furthermore, it is also useful to compare several international studies trend as this study takes a perspective globally. The study provides priceless information and an in-depth integrated view of big data relevant to opportunities and challenges that marketing encountered. Furthermore, it opens new insights into the topic area by emphasizing for further future studies and research directions.

Research on the Application of Big Data in Oil & Gas Industry

Oil & Gas companies are complex, data-driven business with data volumes growing exponentially, however, restricted by traditional IT systems, staff ability and the data quality, the value of the data has not been fully used. With the development of big data technology, the capabilities of big data help oil & gas companies act on large volumes of data, transforming decision-making from reactive to proactive and optimizing all phases of exploration, development and production. In this paper we study the application of big data technology in oil & gas industry, and analysis the best practice of big data in some leading oil & gas companies, such as BP, Shell, and Statoil. Finally, based on our research and study, we provide the suggestion of big data blueprint and construction for oil & gas companies, which can help them to recognize the value of the unexploited data in supporting fact-based decision-making, and to track new business opportunities, reduce costs and reorganize operations.

Personalization and probabilities: Impersonal propensities in online grocery shopping

Accounts of big data practices often assume that they target individuals. Personalization, with all the risks of discrimination and bias it entails, has been the critical focus in accounts of consumption, government, social media, and health. This paper argues that personalization through models using large-scale data is part of a more expansive change in probabilization that, in principle, is not reducible to individual or ‘personal’ attributes and actions. It describes the ‘personalization’ of an online grocery shopping recommender system to list a small number of grocery items of personal relevance for each of the millions of online grocery shoppers at a major UK supermarket chain. Drawing on a theory of probability proposed by the philosopher of science Karl Popper and anthropological work on shopping, it suggests that the attempt to generate personalized predictions necessarily incorporates impersonal relations to others and things. Using a mixture of discourse analysis and code-based reconstruction of key elements of the recommender system, it suggests that personalization is one facet of an open-ended weave of propensities associated with people and things in contemporary big data configurations. The paper explores how, in the context of recommender systems, the constitutive incompleteness of shopping lists, their propensity to expand or change, might be more important than their capacity to be personalized.

Learning Analytics, the Academic Library, and Positive Intellectual Freedom

College and universities are actively developing capacity for learning analytics, which is a sociotechnical system supported by an assemblage of educational data mining technologies and related Big Data practices. Like many Big Data technologies, learning analytics implicates privacy by surveilling behaviors captured by data-based systems and aggregating and analyzing personal information. Issues of privacy are often linked to concerns about intellectual freedom. Consequently, librarians fervently argue for surveillance-free spaces and places to promote the conditions they believe are necessary to support intellectual freedom. In this commentary, I contrast this common view of intellectual freedom with a separate theory, “positive intellectual freedom,” to show how libraries may be able to participate in learning analytics practices while upholding intellectual freedom as a lodestar guiding practice and policy.

Data Protection on the Move: Current Developments in ICT and Privacy/Data Protection, Edited by Serge Gutwirth, Ronald Leenes and Paul de Hert

Data Protection on the Move provides 17 contributions that seek to unearth privacy and data protection issues in the face of technological developments ranging from robotics to Big Data practices and to healthcare. The monograph is mainly the product of the 8th International Conference on Computers, Privacy and Data Protection (CPDP), which is a 3-day long Brussels-based conference that took place between 21 and 23 January 2015. The volume accomplishes what the CPDP aims to do and it brings together junior and senior academics in various fields including law, information technology and mathematics as well as practitioners and policymakers. As acknowledged by the editors, the volume does not follow a thematic structure because it aims to cover a wide range of topics. Many contributions offer legal responses to innovations. In her contribution, Bibi van den Berg discusses the privacy and security risks that internet or cloud connected domestic and service robots entail. The author’s view that air gapping (to disconnect any item, device, or network from the internet if this is not vital for their functioning in order to improve their cybersecurity) can be a solution for minimizing these risks is intriguing, because she challenges the assumption that everything by default has to be connected to the internet. Therefore, the author argues that unless it is vital for its functioning, the novel technologies should not be attached to the internet instead of being designed with the assumption that they will be networked, and therefore being considered under the notion of privacy by design standards. The author makes the case for the functionality question for the domestic and service robots, and yet leaves that question open for any other technologies. Another contribution to robotics discourse comes from Ugo Pagallo, whose chapter seeks to explore the impact of domestic robots in a number of contexts: the US approach to the ‘reasonable expectation of privacy’, the distinction between data controller and data processor, and privacy by design. The author’s contribution touches upon the question on to which data protection framework should manufacturers and designers embed in their products.

A Feeling for the Algorithm: Working Knowledge and Big Data in Biology

The term “Big Data” may serve as a useful marker for particular kinds of questions, practices, and relationships for collecting and using data. Some of the ways of talking about Big Data suggest that there might be something, if not entirely new, then at least importantly different at work in Big Data practices and problem-solving approaches. By using three examples taken from the biomedical sciences—artificial neural networks, the construction of reference genomes, and the usage of the Ensembl database—this essay shows how Big Data practices cannot be understood as mere scaling up of pen-and-paper methods but constitute qualitatively different kinds of knowledge-making practices. These practices are characterized particularly by types of human-computer interaction that are labeled “a feeling for the algorithm.”

A Query-oriented Adaptive Indexing Technique for Smart Grid Big Data Analytics

IoT (Internet of Things) based Smart Grid (SG) is defined as a power grid integrated with a large network of smart objects portrayed by information and communication technology. The data sources of IoT-based SG, as well as their correlations, are usually perplexing, which necessitate indexing techniques for complex queries over the SG dataset to efficiently exploit the rich connotations of data to enable characteristic analytics and fault prediction. As part of popular big data platform, HBase is replacing classic relational data- bases to host huge heterogeneous data records in the form of key-value storage. However, most existing secondary index schemes on HBase are managed and retrieved by corresponding data columns instead of queries to incur inefficiency in answering a complex data query. In this paper, we propose an adaptive indexing technique to speed up a complex data query on HBase for IoT-based SG big data. Our proposed method is based on the observation that most analyses over big power grid data focus on data subsets related to specific power grid events or monitoring data instead of the whole dataset. Theoretical analysis and experimental test show that the proposed query-oriented secondary indexing scheme is feasible in improving the query performance. For a join operation, when compared with a query scheme without secondary indexing, our proposed indexing scheme outperforms from a minimum 6.54 × speedup to a maximum 860 × speedup; when compared with a classic secondary indexing scheme implemented on HBase, our indexing scheme outperforms from a minimum 1.20 × speedup to a maximum 8.68 × speedup. Our indexing technique would be a useful reference for other industrial big data practices.

Big Data, urban governance, and the ontological politics of hyperindividualism

Big Data’s calculative ontology relies on and reproduces a form of hyperindividualism in which the ontological unit of analysis is the discrete data point, the meaning and identity of which inheres in itself, preceding, separate, and independent from its context or relation to any other data point. The practice of Big Data governed by an ontology of hyperindividualism is also constitutive of that ontology, naturalizing and diffusing it through practices of governance and, from there, throughout myriad dimensions of everyday life. In this paper, I explicate Big Data’s ontology of hyperindividualism by contrasting it to a coconstitutive ontology that prioritizes relationality, context, and interdependence. I then situate the ontology of hyperindividualism in its genealogical context, drawing from Patrick Joyce’s history of liberalism and John Dewey’s pragmatist account of individualism, liberalism, and social action. True to its genealogical provenance, Big Data’s ontological politics of hyperindividualism reduces governance to the management of atomistic behavior, undermines the contribution of urban complexity as a resource for governance, erodes the potential for urban democracy, and eviscerates the possibility of collective resistance.

Big data and technology assessment: research topic or competitor?

ABSTRACTWith its promise to transform how we live, work, and think, Big Data has captured the imaginations of governments, businesses, and academia. However, the grand claims of Big Data advocates have been accompanied with concerns about potential detrimental implications for civil rights and liberties, leading to a climate of clash and mutual distrust between different stakeholders. Throughout the years, the interdisciplinary field of technology assessment (TA) has gained considerable experience in studying socio-technical controversies and as such is exceptionally well equipped to assess the premises and implications of Big Data practices. However, the relationship between Big Data as a socio-technical phenomenon and TA as a discipline assessing such phenomena is a peculiar one: Big Data may be the first topic TA deals with that is not only an object of inquiry, but also a major competitor, rivaling TA in several of its core functions, including the assessment of public views and visions, means and methods for exploring the future, and the provision of actionable knowledge and advice for political decision-making. Our paper explores this dual relationship between Big Data and TA before concluding with some considerations on how TA might contribute to more responsible data-based research and innovation.

Practicing, materialising and contesting environmental data

While there are now an increasing number of studies that critically and rigorously engage with Big Data discourses and practices, these analyses often focus on social media and other forms of online data typically generated about users. This introduction discusses how environmental Big Data is emerging as a parallel area of investigation within studies of Big Data. New practices, technologies, actors and issues are concretising that are distinct and specific to the operations of environmental data. Situating these developments in relation to the seven contributions to this special collection, the introduction outlines significant characteristics of environmental data practices, data materialisations and data contestations. In these contributions, it becomes evident that processes for validating, distributing and acting on environmental data become key sites of materialisation and contestation, where new engagements with environmental politics and citizenship are worked through and realised.

Environmental justice in the age of big data: challenging toxic blind spots of voice, speed, and expertise

In recent years, grassroots environmental justice activists have increasingly used big data techniques for monitoring, recording, and reporting toxic environmental exposures. Despite the promise of big data for environmental justice, there is a need to address structural barriers to making toxic environmental exposures visible, and to avoid over-relying on new digital methods and techniques as a panacea for problems of voice. The emphasis of real-time analysis in crowdsourced and participatory big data is good at tracking the immediate aftermath of environmental disasters, but it misses slower-burning environmental problems that emerge over time. While big data more generally may have implications for understanding toxic exposure landscapes across different temporal and spatial scales, it is complex, difficult to analyze, and faces significant problems of reliability. There are three key blind spots of the ethos and practice of big data in relation to environmental justice: voice, speed, and expertise. In the context of increasing pressure to embrace new tools and technologies, it is also important to slow down and to reflect on the wider implications of the age of big data.

Big Data: Issues for an International Political Sociology of Data Practices: Table 1.

The claim that big data can revolutionize strategy and governance in the context of international relations is increasingly hard to ignore. Scholars of international political sociology have mainly discussed this development through the themes of security and surveillance. The aim of this paper is to outline a research agenda that can be used to raise a broader set of sociological and practice-oriented questions about the increasing datafication of international relations and politics. First, it proposes a way of conceptualizing big data that is broad enough to open fruitful investigations into the emerging use of big data in these contexts. This conceptualization includes the identification of three moments contained in any big data practice. Secondly, it suggests a research agenda built around a set of sub-themes that each deserve dedicated scrutiny when studying the interplay between big data and international relations along these moments. Through a combination of these moments and sub-themes, the paper suggests a roadmap for an international political sociology of the datafication of worlds.

Big Data Surveillance and the Body-subject

This paper considers the implications of big data practices for theories about the surveilled subject who, analysed from afar, is still gazed upon, although not directly watched as with previous surveillance systems. We propose this surveilled subject be viewed through a lens of proximity rather than interactivity, to highlight the normative issues arising within digitally mediated relationships. We interpret the ontological proximity between subjects, data flows and big data surveillance through Merleau-Ponty’s ideas combined with Levinas’ approach to ethical proximity and Coeckelberg’s work on proximity in the digital age. This leads us to highlight how competing normativities, and normative dilemmas in these proximal spaces, manipulate the surveilled subject’s embodied practices to lead the embodied individual towards experiencing them in a local sense. We explore when and how the subject notices these big data practices and then interprets them through translating their experiences into courses of action, inaction or acquiescence.