scholarly journals Big Data, Algorithmic Regulation, and the History of the Cybersyn Project in Chile, 1971–1973

2018 ◽  
Vol 7 (4) ◽  
pp. 65 ◽  
Author(s):  
Katharina Loeber
Keyword(s):  
Big Data ◽  
History Of

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

scholarly journals Teaching and Researching the History of Medicine in the Era of (Big) Data: Introduction

2016 ◽  
Vol 61 (1) ◽  
pp. 176-176
Author(s):  
Frederick W. Gibbs ◽  
Jeffrey S. Reznick
Keyword(s):  
Big Data ◽  
History Of Medicine ◽  
History Of

scholarly journals Towards Big Data: Digitising Economic and Business History

2020 ◽  
pp. 45-67
Author(s):  
Jari Eloranta ◽  
Pasi Nevalainen ◽  
Jari Ojala
Keyword(s):  
Big Data ◽  
Data Gathering ◽  
Business History ◽  
General State ◽  
Research Projects ◽  
Digital History ◽  
History Data ◽  
Digital Era ◽  
Research Outcomes ◽  
History Of

This chapter describes the experiences in computational and digital history of economic and business historians who for decades have been forerunners in digital history data gathering and computational analysis. It attempts to discuss the major developments within this area internationally and, in some specific cases, in Finland in the fields of digital economic and business history. It concentrates on a number of research projects that the authors have previously been involved in, as well as research outcomes by other economic and business historians within Finland and elsewhere. It is not claimed that the projects discussed are unique or ahead of their time in the field of economic and business history—on the contrary they are representing a more general state of the art within the field and used as illustrative cases illuminating the possibilities and challenges facing historians in the digital era.

Drug Prediction in Healthcare Using Big Data and Machine Learning

2019 ◽  
pp. 79-92 ◽  
Author(s):  
Mamoon Rashid ◽  
Vishal Goyal ◽  
Shabir Ahmad Parah ◽  
Harjeet Singh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Side Effects ◽  
Previous History ◽  
Distributed File System ◽  
Optimal Drug ◽  
Hadoop Distributed File System ◽  
History Of ◽  
Medicinal Drugs

The healthcare system is literally losing patients due to improper diagnosis, accidents, and infections in hospitals alone. To address these challenges, the authors are proposing the drug prediction model that will act as informative guide for patients and help them for taking right medicines for the cure of particular disease. In this chapter, the authors are proposing use of Hadoop distributed file system for the storage of medical datasets related to medicinal drugs. MLLib Library of Apache Spark is to be used for initial data analysis for drug suggestions related to symptoms gathered from particular user. The model will analyze the previous history of patients for any side effects of the drug to be recommended. This proposal will consider weather and maps API from Google as well so that the patients can easily locate the nearby stores where the medicines will be available. It is believed that this proposal of research will surely eradicate the issues by prescribing the optimal drug and its availability by giving the location of the retailer of that drug near the customer.

Introduction to Big Data and Business Analytics

2022 ◽  
pp. 67-76
Author(s):  
Dineshkumar Bhagwandas Vaghela
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
Complex Data ◽  
Business Analytics ◽  
Database Applications ◽  
Complex Data Sets ◽  
History Of ◽  
Rapid Generation

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

Library Catalogue Records as a Research Resource: Introducing ‘A Big Data History of Music’

2016 ◽  
Vol 63 (2) ◽  
pp. 67-88 ◽  
Author(s):  
Sandra Tuppen ◽  
Stephen Rose ◽  
Loukia Drosopoulou
Keyword(s):  
Big Data ◽  
History Of ◽  
Data History ◽  
Library Catalogue

scholarly journals How should we do the history of Big Data?

2016 ◽  
Vol 3 (1) ◽  
pp. 205395171664613 ◽  
Author(s):  
David Beer
Keyword(s):  
Big Data ◽  
History Of

scholarly journals Text-Mining and the History of Medicine: Big Data, Big Questions?

2016 ◽  
Vol 60 (2) ◽  
pp. 294-296 ◽  
Author(s):  
Elizabeth Toon ◽  
Carsten Timmermann ◽  
Michael Worboys
Keyword(s):  
Big Data ◽  
Text Mining ◽  
History Of Medicine ◽  
History Of

scholarly journals HR Metrics and Talent Analytics

2017 ◽  
Author(s):  
Alexis A. Fink ◽  
Michael C. Sturman
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Human Resource ◽  
Investment Decisions ◽  
Research And Practice ◽  
Hr Practices ◽  
History Of Research ◽  
Decision Points ◽  
History Of ◽  
Current Practices

Taking advantage of the history of research quantifying the effects of human resource (HR) practices, leveraging current advances in information systems and opportunities presented by big data, HR metrics and talent analytics present a renewed opportunity to help drive effective HR practices. HR metrics are operational measures, addressing how efficient, effective, and impactful an organization’s HR practices are. In contrast, talent analytics focus on decision points, guiding investment decisions. The chapter provides an overview of the historic roots and current practices around HR metrics and talent analytics. Through this, we explore the role, benefits, and risks of benchmarking and utility analyses as two common approaches to setting HR metrics. We discuss how current advances in research and practice make the use of HR metrics and talent analytics a greater business necessity. We conclude the chapter with a discussion on fostering talent analytics within organizations.

scholarly journals The Future of Welfare History: the Local Perspective

2018 ◽  
pp. 33-42
Author(s):  
Alysa Levene
Keyword(s):  
Big Data ◽  
Social Structure ◽  
Regional Studies ◽  
Short Article ◽  
Local Populations ◽  
The Past ◽  
Local Case ◽  
History Of ◽  
Welfare History ◽  
Local Perspectives

This short article examines what local perspectives have added—and continue to add—to welfare history. The paper begins by summarising the work of the Cambridge Group for the History of Population and Social Structure on households and the ways they functioned and shows how work on local populations set many of today's research agendas. It then argues that a fruitful way forward might be to use national and regional studies to identify local case studies that would be particularly interesting or informative. This point is illustrated by discussing a range of examples. The article concludes with a discussion of the implications of 'big' data and digitisation for local studies of welfare in the past.

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