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

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.

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Assessment of Normal Sagittal Alignment of the Spine and Pelvis in Children and Adolescents

BioMed Research International ◽

10.1155/2013/842624 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 14

Author(s):

Hasan Ghandhari ◽

Hamid Hesarikia ◽

Ebrahim Ameri ◽

Abolfazl Noori

Keyword(s):

Children And Adolescents ◽

Sagittal Balance ◽

Sagittal Alignment ◽

Demographic Data ◽

Lateral View ◽

Old People ◽

Spinopelvic Parameters ◽

Spinopelvic Balance ◽

History Of ◽

Data History

Aim. We aimed to determine spinopelvic balance in 8–19-year-old-people in order to assess pelvic and spinal parameters in sagittal view.Methods. Ninety-eight healthy students aged 8–19 years, who lived in the central parts of Tehran, were assessed. Demographic data, history of present and past diseases, height (cm), and weight (kg) were collected. Each subject was examined by an orthopedic surgeon and spinal radiographs in lateral view were obtained. Eight spinopelvic parameters were measured by 2 orthopedic spine surgeons.Results. Ninety-eight subjects, among which 48 were girls (49%) and 50 boys (51%), with a mean age of13.6±2.9years (range: 8–19) were evaluated. Mean height and weight of children were153.6±15.6cm and49.9±13.1kgs, respectively. Mean TK, LL, TT, LT, and PI of subjects were 37.1 ± 9.9°, 39.6 ± 12.4°, 7.08 ± 4.9°, 12.0 ± 5.9°, and 45.37 ± 10.7°, respectively.Conclusion. Preoperation planning for spinal fusion surgeries via applying PI seems reasonable. Predicating “abnormal” to lordosis and kyphosis values alone without considering overall sagittal balance is incorrect. Mean of SS and TK in our population is slightly less than that in Caucasians.

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

Medical History ◽

10.1017/mdh.2016.104 ◽

2016 ◽

Vol 61 (1) ◽

pp. 176-176

Author(s):

Frederick W. Gibbs ◽

Jeffrey S. Reznick

Keyword(s):

Big Data ◽

History Of Medicine ◽

History Of

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

Social Sciences ◽

10.3390/socsci7040065 ◽

2018 ◽

Vol 7 (4) ◽

pp. 65 ◽

Cited By ~ 1

Author(s):

Katharina Loeber

Keyword(s):

Big Data ◽

History Of

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Towards Big Data: Digitising Economic and Business History

Digital Histories: Emergent Approaches within the New Digital History ◽

10.33134/hup-5-3 ◽

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.

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Drug Prediction in Healthcare Using Big Data and Machine Learning

Advances in Social Networking and Online Communities - Hidden Link Prediction in Stochastic Social Networks ◽

10.4018/978-1-5225-9096-5.ch005 ◽

2019 ◽

pp. 79-92 ◽

Cited By ~ 1

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.

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Introduction to Big Data and Business Analytics

10.4018/978-1-6684-3662-2.ch004 ◽

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.

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