1,048,531 research outputs found
Big data architecture for pervasive healthcare: a literature review
Pervasive healthcare aims to deliver deinstitutionalised healthcare services to patients anytime and anywhere. Pervasive healthcare involves remote data collection through mobile devices and sensor network which the data is usually in large volume, varied formats and high frequency. The nature of big data such as volume, variety, velocity and veracity, together with its analytical capabilities com-plements the delivery of pervasive healthcare. However, there is limited research in intertwining these two domains. Most research focus mainly on the technical context of big data application in the healthcare sector. Little attention has been paid to a strategic role of big data which impacts the quality of healthcare services provision at the organisational level. Therefore, this paper delivers a conceptual view of big data architecture for pervasive healthcare via an intensive literature review to address the aforementioned research problems. This paper provides three major contributions: 1) identifies the research themes of big data and pervasive healthcare, 2) establishes the relationship between research themes, which later composes the big data architecture for pervasive healthcare, and 3) sheds a light on future research, such as semiosis and sense-making, and enables practitioners to implement big data in the pervasive healthcare through the proposed architecture
Veracity Roadmap: Is Big Data Objective, Truthful and Credible?
This paper argues that big data can possess different characteristics, which affect its quality. Depending on its origin, data processing technologies, and methodologies used for data collection and scientific discoveries, big data can have biases, ambiguities, and inaccuracies which need to be identified and accounted for to reduce inference errors and improve the accuracy of generated insights. Big data veracity is now being recognized as a necessary property for its utilization, complementing the three previously established quality dimensions (volume, variety, and velocity), But there has been little discussion of the concept of veracity thus far. This paper provides a roadmap for theoretical and empirical definitions of veracity along with its practical implications. We explore veracity across three main dimensions: 1) objectivity/subjectivity, 2) truthfulness/deception, 3) credibility/implausibility – and propose to operationalize each of these dimensions with either existing computational tools or potential ones, relevant particularly to textual data analytics. We combine the measures of veracity dimensions into one composite index – the big data veracity index. This newly developed veracity index provides a useful way of assessing systematic variations in big data quality across datasets with textual information. The paper contributes to the big data research by categorizing the range of existing tools to measure the suggested dimensions, and to Library and Information Science (LIS) by proposing to account for heterogeneity of diverse big data, and to identify information quality dimensions important for each big data type
Exploratory Analysis of Pairwise Interactions in Online Social Networks
In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4
on pages 422 to 42
Modeling the Impact of Big Data Analysis Investments on the Dynamics of Customer Acquisition: A Case Study of Telecommunication Sector in the United States
Postponed access: the file will be accessible after 2022-08-14In the age of data explosion, many firms are heavily investing in big data and big data analytics (BDA) without being able to anticipate how much value they will receive. Thus, there is a growing body of research that has been focusing on the impact of big data and BDA investments on firm performance. Nevertheless, most of these studies use self-reported data and none of them has addressed the dynamics in the firm outcomes as well as the continuous feedback processes between BDA investment, firm performance, and other intermediate variables. In this thesis, I collected data about two telecommunication firms in the U.S., namely T-Mobile and Verizon, to build up a system dynamics model that helps to answer two research questions that have not been properly investigated hitherto: 1) How do BDA investments dynamically influence firm performance? and 2) Which policies can help large and small firms to enhance the outcomes of their BDA investments? My simulation results reveal that when the industry develops in favor of BDA activities (i.e., lower data acquisition and data storage costs, more data generated by customers), small firms will be put at a disadvantage. In contrast, large firms with larger customer bases will be able to exploit their economies of scale in BDA investments to quickly increase their market share and gain higher profits. Thus, large firms are advised to increase their investments in BDA and data acquisition, in addition to increase their data volume more quickly even at the cost of lower data quality. As an increase in data volume will typically lead to a decrease in data storage cost, this policy will help large firms effectively increase their total number of customers, which will lead to a further decrease in the data acquisition cost, resulting in higher firm revenues and firm profits. Small firms, instead, are advised to sacrifice their profits for market share. Specifically, they should invest more heavily than large firms to lift the volume of their data up to the point that it can nullify the cost advantage of large firms. It is unclear that, though, whether small firms can survive when making such a big trade-off. Future research might explore whether the intervention from governments might help resolve this inequality between small and large firms.Master's Thesis in System DynamicsGEO-SD351MASV-SYSD
Corpus linguistics as digital scholarship : Big data, rich data and uncharted data
This introductory chapter begins by considering how the fields of corpus linguistics, digital linguistics and digital humanities overlap, intertwine and feed off each other when it comes to making use of the increasing variety of resources available for linguistic research today. We then move on to discuss the benefits and challenges of three partly overlapping approaches to the use of digital data sources: (1) increasing data size to create “big data”, (2) supplying multi-faceted co(n)textual information and analyses to produce “rich data”, and (3) adapting existing data sets to new uses by drawing on hitherto “uncharted data”. All of them also call for new digital tools and methodologies that, in Tim Hitchcock’s words, “allow us to think small; at the same time as we are generating tools to imagine big.” We conclude the chapter by briefly describing how the contributions in this volume make use of their various data sources to answer new research questions about language use and to revisit old questions in new ways.Peer reviewe
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