Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory

2022 ◽  
Vol 175 ◽  
pp. 121425
Author(s):  
Dmitry Zhukov ◽  
Tatiana Khvatova ◽  
Carla Millar ◽  
Elena Andrianova
Keyword(s):  
Big Data ◽  
Stochastic Models ◽  
New Techniques ◽  
Self Organisation

Habemus Corpora: Reapproaching Philological Problems in the Age of ‘Big’ Data

2021 ◽  
Vol 139 (1) ◽  
pp. 94-127
Author(s):  
Mark Faulkner
Keyword(s):  
Big Data ◽  
Old English ◽  
English Language ◽  
Historical Research ◽  
British Library ◽  
New Techniques ◽  
Major Task ◽  
New Methodologies ◽  
Primary Focus

Abstract This paper demonstrates the potential of new methodologies for using existing corpora of medieval English to better contextualise linguistic variants, a major task of philology and a key underpinning of our ability to answer major literary-historical questions, such as when, where and to what purpose medieval texts and manuscripts were produced. The primary focus of the article is the assistance these methods can offer in dating the composition of texts, which it illustrates with a case study of the “Old” English Life of St Neot, uniquely preserved in the mid-twelfth-century South-Eastern homiliary, London, British Library, Cotton Vespasian D.xiv, fols. 4–169. While the Life has recently been dated around 1100, examining its orthography, lexis, syntax and style alongside that of all other English-language texts surviving from before 1150 using new techniques for searching the Dictionary of Old English Corpus suggests it is very unlikely to be this late. The article closes with some reflections on what book-historical research should prioritise as it further evolves into the digital age.

Security Vulnerabilities, Threats, and Attacks in IoT and Big Data

2020 ◽  
pp. 141-167
Author(s):  
Prabha Selvaraj ◽  
Sumathi Doraikannan ◽  
Vijay Kumar Burugari
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Web Interface ◽  
Security Vulnerabilities ◽  
New Techniques ◽  
Iot Security ◽  
Web Interfaces ◽  
The Web

Big data and IoT has its impact on various areas like science, health, engineering, medicine, finance, business, and mainly, the society. Due to the growth in security intelligence, there is a requirement for new techniques which need big data and big data analytics. IoT security does not alone deal with the security of the device, but it also has to care about the web interfaces, cloud services, and other devices that interact with it. There are many techniques used for addressing challenges like privacy of individuals, inference, and aggregation, which makes it possible to re-identify individuals' even though they are removed from a dataset. It is understood that a few security vulnerabilities could lead to insecure web interface. This chapter discusses the challenges in security and how big data can be used for it. It also analyzes the various attacks and threat modeling in detail. Two case studies in two different areas are also discussed.

scholarly journals Aspects of Discourse Stream Analysis

2019 ◽  
Vol IV (II) ◽  
pp. 1-6
Author(s):  
Mark Perkins
Keyword(s):  
Big Data ◽  
Real Time ◽  
Text Analysis ◽  
Large Scale ◽  
Relative Speed ◽  
Fake News ◽  
New Techniques ◽  
Subject Orientation ◽  
Theory Of Discourse ◽  
Theoretical Developments

The huge proliferation of textual (and other data) in digital and organisational sources has led to new techniques of text analysis. The potential thereby unleashed may be underpinned by further theoretical developments to the theory of Discourse Stream Analysis (DSA) as presented here. These include the notion of change in the discourse stream in terms of discourse stream fronts, linguistic elements evolving in real time, and notions of time itself in terms of relative speed, subject orientation and perception. Big data has also given rise to fake news, the manipulation of messages on a large scale. Fake news is conveyed in fake discourse streams and has led to a new field of description and analysis.

Sentiment Analysis of Twitter Data

2019 ◽  
Vol 14 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Ankit Srivastava ◽  
Vijendra Singh ◽  
Gurdeep Singh Drall
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Hybrid Approach ◽  
Naïve Bayes ◽  
Data Sets ◽  
The Novel ◽  
New Techniques ◽  
Hidden Knowledge

Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.

Smart Stadium as a Laboratory of Innovation: Technology, Sport, and Datafied Normalization of the Fans

2020 ◽  
pp. 216747952094357
Author(s):  
Chamee Yang ◽  
C. L. Cole
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
The United States ◽  
New Techniques ◽  
Critical Studies ◽  
Social Ordering ◽  
Definition Of ◽  
Smart Technologies ◽  
Contemporary Development ◽  
The Relationship

This article addresses the relationship between the contemporary development of the “smart” stadium and changing norms of innovation in sports. Given the evolving forms of smart technologies blurring the boundaries between the actual and mediated domains of sports, an approach that grapples with the broad sociotechnical dynamics within and around sport is necessary. Drawing from critical studies on big data, innovation, and smart cities, this study adopts a sociotechnical perspective to approach Arizona State University’s Sun Devil Stadium, known as one of the first smart stadiums in the United States. This study examines how the smart stadium employs a range of techniques and technologies to engage with and influence broader sociocultural themes in society: the prevalent imperative of innovation and the hyperdigitalization of sport through which bodies in space are becoming knowable and governable in new ways. We conclude that the smart stadium, articulated both literally and figuratively as a “living laboratory of innovation,” appropriates sport as a useful motif to affect broader cultural debates around big data and spatializes new techniques of social ordering through a parametric and processual definition of normalcy.

Big Data Analysis and Implementation in Different Areas Using IoT

2017 ◽  
Vol 1 (2) ◽  
pp. 12-25
Author(s):  
Aqeel ur Rehman ◽  
Muhammad Fahad ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Healthcare Applications ◽  
New Techniques ◽  
Standard Data ◽  
Huge Data ◽  
Data Rates ◽  
Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

Promoting Social and Solidarity Economy Through Big Data

Web Services ◽  
2019 ◽  
pp. 1967-1990
Author(s):  
David De la Antonia López
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Computing Technology ◽  
Solidarity Economy ◽  
New Techniques ◽  
Small Organizations ◽  
Complete Development ◽  
New Situation ◽  
Economic Capacity

The aim of this chapter is to describe how to implement a strategy of Big Data to boost Social and Solidarity Economy (SSE). Because of reduction of prices in ICT systems, computing technology has changed and new techniques for distributed computing are the mainstream. With the evolution, it is now possible to manage immense volumes of data that previously could have only been handled by supercomputers at great expense. Through better analysis of the large volumes of data that are becoming available, there is the potential for making faster advances and improving the profitability of many enterprises. Thus, large companies can invest more money into these tools and consequently have more opportunities in obtaining good results. This new situation will widen the gap between large and small organizations, mainly those organizations of modest economic capacity as those that belong to SSE. Therefore, in this research we have made a complete development of software, techniques and tools for implementing a Big Data in SSE. It will help them to narrow the gap with large organizations.

scholarly journals Big Data in Medical Research and Application

2018 ◽  
Vol 176 ◽  
pp. 03017
Author(s):  
Teng Yilin ◽  
Cao Gaofang ◽  
Wang Rui
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Pharmaceutical Research ◽  
Biomedical Science ◽  
New Techniques ◽  
Data Points ◽  
Gene Database ◽  
Data Statistics ◽  
Medical Big Data ◽  
Human Awareness

With the advanced instruments and information technology integrated in biomedical science more and more extensively, the advent of the big data era has had a significant impact on biomedical research, making human awareness of themselves and diseases more profound. The future medicine tends to combine data and medicine, to master gene database and medical human disease data, then to apply data statistics and analysis and application in healthcare. New techniques of big data in medicine are bound to make medical research and application more predictable. This paper introduces the main sources and characteristics of medical big data, points out the necessity of big data research in medical field, summarizes the current research in medical big data and its application in disease prediction, clinical assistance and pharmaceutical research and development. In other aspects, it analyses the problems of medical big data in applied research.

Big Data Analysis in IoT

2015 ◽  
pp. 313-327
Author(s):  
Aqeel-ur Rehman ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Big Data Analysis ◽  
Data Generation ◽  
New Techniques ◽  
Huge Data ◽  
Data Rates ◽  
Huge Challenge

In IoT, data management is a big problem due to the connectivity of billions of devices, objects, processes generating big data. Since the Things are not following any specific (common) standard, so analysis of such data becomes a big challenge. There is a need to elaborate about the characteristics of IoT based data to find out the available and applicable solutions. Such kind of study also directs to realize the need of new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates and formats it is getting a huge challenge to deal with such variety of data. As IoT is providing processing nodes in quantity in form of smart nodes, it is presenting itself a good platform for big data analysis. In this chapter, characteristics of big data and requirements for big data analysis are highlighted. Considering the big source of data generation as well as the plausible suitable platform of such huge data analysis, the associated challenges are also underlined.

Advances in Visualization for Theory Testing in Environmental Criminology

2018 ◽  
Author(s):  
Patricia L. Brantingham ◽  
Paul J. Brantingham ◽  
Justin Song ◽  
Valerie Spicer
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Visual Image ◽  
Theory Testing ◽  
Environmental Criminology ◽  
Data Sets ◽  
New Techniques ◽  
Research Results ◽  
Modern Information ◽  
Answering Questions

This chapter discusses advances in visualization for environmental criminology. The environment within which people move has many dimensions that influence or constrain decisions and actions by individuals and by groups. This complexity creates a challenge for theoreticians and researchers in presenting their research results in a way that conveys the dynamic spatiotemporal aspects of crime and actions by offenders in a clearly understandable way. There is an increasing need in environmental criminology to use scientific visualization to convey research results. A visual image can describe underlying patterns in a way that is intuitively more understandable than text and numeric tables. The advent of modern information systems generating large and deep data sets (Big Data) provides researchers unparalleled possibilities for asking and answering questions about crime and the environment. This will require new techniques and methods for presenting findings and visualization will be key.

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