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Big Data-Enabled Nursing

Attention on “big data” spans nursing and the health sciences, and extends as well to engineering/computer sciences through to the liberal arts in professional literature. A current Google search (3 Nov 2016) of “big data” yields 288 million entries. A focused search of “big data and nursing” yields more than 3.9 million entries. Thus we ask, “Why big data? Why nursing?” The focus of this chapter is to provide an overview of why big data has emerged now and to make the case for how big data has the capacity to change health, healthcare systems, and nursing. This chapter lays a foundation for the chapters and case studies to follow that explore what data, knowledge, and transformation processes are needed to put information and knowledge into the hands of nursing wherever nurses are working. In this chapter we examine the big data sources within and beyond nursing and healthcare that can be collected and analyzed to improve nursing and patient, family and community health. This chapter entices the reader to examine “Why big data now?” and “Why big data in the future?” This chapter is meant to stir curiosity for “Why should I be knowledgeable?” Whether the reader’s role is in clinical practice, education, research, industry, or policy, the applied uses of big data analytics are empowering change at an exponential speed across all domains. Big data has the capacity to illuminate nursing’s discovery of new knowledge and best practices that are safe, effective and lead to improved outcomes including well-being of providers; it also can expand nursing’s vision and future possibilities through increasing awareness of what nursing doesn’t know. The importance of nursing’s lens on the new discoveries obtained through big data and data science is critical to the transformation of health and healthcare systems. This transformation completes the challenge of placing the person at the center of all care initiatives and actions.

Big Data: The Management Revolution

There’s much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance. Consider retailing. Booksellers in physical stores could always track which books sold and which did not. If they had a loyalty program, they could tie some of those purchases to individual customers. And that was about it. Once shopping moved online, though, the understanding of customers increased dramatically. Online retailers could track not only what customers bought, but also what else they looked at; how they navigated through the site; how much they were influenced by promotions, reviews, and page layouts; and similarities across individuals and groups. Before long, they developed algorithms to predict what books individual customers would like to read next—algorithms that performed better every time the customer responded to or ignored a recommendation. Traditional retailers simply couldn’t access this kind of information, let alone act on it in a timely manner. It’s no wonder that Amazon has put so many brick-and-mortar bookstores out of business.


Purpose  The purpose of this paper is to forward an extension of reception analysis as a way to incorporate and give insight to social media mediations and big data in a qualitative marketing perspective .We propose a research method that focuses on discursive developments in consumer debates for example on YouTube  a large-scale openaccess social media platform  as opposed to the closed and tightknit communities investigated by netnography. Methodology/approach  Online reception analysis Findings  Using a combination of qualitative and quantitative methods, we find that big data can enrich online reception analyses by showing new aspects of weak tie online networks and consumers meaning making. Research limitations/implications  The potential of online reception analysis is to encompass a discursive perspective on consumer interactions on large-scale open-access social media and to be able to analyze socialities that do not represent shared cultures but are more representative of transitory everyday interactions. Originality/value of paper  Our method contributes to the current focus to define levels of analysis beyond research centered on individuals and individual interactions within groups to investigate other larger socialities. Further, our method also contributes by incorporating and investigating the mediatization of interaction that social media contributes with and therefore our methods actively work with the possibilities of social media. Hence, by extending the advances made by netnography into online spaces, online reception analysis can potentially inform the current status of big data research with a sociocultural methodological perspective .

Validating viral marketing strategies in Twitter via Agent-based Social Simulation

A number of marketing phenomena are too complex for conventional analytical or empirical approaches. This makes marketing a costly process of trial and error: proposing, imagining, trying in the real world, and seeing results. Alternatively, Agent-based Social Simulation (ABSS) is becoming the most popular approach to model and study these phenomena. This research paradigm allows modeling a virtual market to: design, understand, and evaluate marketing hypotheses before taking them to the real world. However, there are shortcomings in the specialized literature such as the lack of methods, data, and implemented tools to deploy realistic virtual market with ABSS. To advance the state of the art in this complex and interesting problem, this paper is a seven-fold contribution based on a (1) method to design and validate viral marketing strategies in Twitter by ABSS. The method is illustrated with the widely studied problem of rumor diffusion in social networks. After (2) an extensive review of the related works for this problem, (3) an innovative spread model is proposed which rests on the exploratory data analysis of two different rumor datasets in Twitter. Besides, (4) new strategies are proposed to control malicious gossips. (5) The experimental results validate the realism of this new propagation model with the datasets and (6) the strategies performance is evaluated over this model. (7) Finally, the article is complemented by a free and open-source simulator.

Big Data: A Survey

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

A Survey of Security and Privacy in Big Data

Big data has been arising a growing interest in both scientific and industrial fields for its potential value. However, before employing big data technology into massive applications, a basic but also principle topic should be investigated: security and privacy. In this paper, the recent research and development on security and privacy in big data is surveyed. First, the effects of characteristics of big data on information security and privacy are described. Then, topics and issues on security are discussed and reviewed. Further, privacy-preserving trajectory data publishing is studied due to its future utilization, especially in telecom operation.

Research note: Machinery, manumission, and economic machinations

This research note reports upon advancements in computerization and big data creation within the off- highway plant and machinery sector. A thematic literature review synthesizes a disparate range of research initiatives and industrial developments and highlights specific examples of technological developments. A discussion regarding impact upon future employment concludes that rather than creating mass unemployment, computerization will change the employment horizon and continue to shape the global economic community. Education is quintessentially important to humanity which must master the machine and not become a slave to technology. Future proofing of educational provisions will therefore feature heavily in tomorrow’s employment market. This provocative research note advances new ideas and theoretical perspectives that are specifically designed to stimulate academic debate in this novel and rapidly developing area of scientific endeavor.

A big data analytics framework for scientific data management

The Ophidia project is a research effort addressing big data analytics requirements, issues, and challenges for
eScience. We present here the Ophidia analytics framework, which is responsible for atomically processing, transforming and manipulating array-based data. This framework provides a common way to run on large clusters analytics tasks applied to big datasets. The paper highlights the design principles, algorithm, and most relevant implementation aspects of the Ophidia analytics framework. Some experimental results, related to a couple of data analytics operators in a real cluster environment, are also presented.

A Secure Cloud Computing Based Framework for Big Data Information Management of Smart Grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call “Smart-Frame.” The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

NoSQL Systems for Big Data Management

The advent of Big Data created a need for out-of-the-box horizontal scalability for data management systems. This ushered in an array of choices for Big Data management under the umbrella term NoSQL. In this paper, we provide a taxonomy and unified perspective on NoSQL systems. Using this perspective, we compare and contrast various NoSQL systems using multiple facets including system architecture, data model, query language, client API, scalability, and availability. We group current NoSQL systems into seven broad categories: Key-Value,Table-type/Column, Document, Graph, Native XML, Native Object, and Hybrid databases. We also describe application scenarios for each category to help the reader in choosing an appropriate NoSQL system for a given application. We conclude the paper by indicating future research directions.

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