The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)

Sequential Bayesian updating for Big Data

The velocity, volume, and variety of big data present both challenges and opportunities for cognitive science. We introduce sequential Bayesian updat-ing as a tool to mine these three core properties. In the Bayesian approach, we summarize the current state of knowledge regarding parameters in terms of their posterior distributions, and use these as prior distributions when new data become available. Crucially, we construct posterior distributions in such a way that we avoid having to repeat computing the likelihood of old data as new data become available, allowing the propagation of information without great computational demand. As a result, these Bayesian methods allow continuous inference on voluminous information streams in a timely manner. We illustrate the advantages of sequential Bayesian updating with data from the MindCrowd project, in which crowd-sourced data are used to study Alzheimer’s Dementia. We fit an extended LATER (Linear Ap-proach to Threshold with Ergodic Rate) model to reaction time data from the project in order to separate two distinct aspects of cognitive functioning: speed of information accumulation and caution

Best Practices in Accelerating the Data Science Process in Python

The number of data science and big data projects is growing, and current software development approaches are challenged to support and contribute to the success and frequency of these projects. Much has been researched on how data science algorithm is used and the benefits of big data, but very little has been written about what best practices can be leveraged to accelerate and effectively deliver data science and big data projects. Big data characteristics such as volume, variety, velocity, and veracity complicate these projects. The proliferation of open-source technologies available to data scientists can also complicate the landscape. With the increase in data science and big data projects, organizations are struggling to deliver successfully. This paper addresses the data science and big data project process, the gaps in the process, best practices, and how these best practices are being applied in Python, one of the common data science open-source programming languages

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Transplant Open Registry Initiative

Health data science aims to extract knowledge from data allowing better decision-making, using multidisciplinary approaches from fields such as computation, statistics, epidemiology, and several medical knowledge domains. We live in the ‘big data’ era, with a growing availability of health data, in volume, variety, and velocity, also for tasks such as kidney transplantation. Hereby, secondary use of this health data must be encouraged to improve patient care planning, disease research, and policymaking around transplantation. This article presents the Transplant Open Registry (TxOR) website where some health data science applications on kidney transplantation are available. With it, we try to answer, some of the remaining questions on kidney transplantation in Portugal, with a new approach.This project received the “Antonio Morais Sar- mento” research grant from the Portuguese Society of Transplantation.info:eu-repo/semantics/publishedVersio

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Data Science Solution for User Authentication

User authentication is considered a key factor in almost any software system and is often the first layer of security in the digital world. Authentication methods utilize one, or a combination of up to two, of the following factors: something you know, something you have and something you are. To prevent serious data breaches that have occurred using the traditional authentication methods, a fourth factor, something you do, that is being discussed among researchers; unfortunately, methods that rely on this fourth factor have problems of their own. This thesis addresses the issues of the fourth authentication factor and proposes a data science solution for user authentication. The new solution is based on something you do and relies on analytic techniques to transfer Big data characteristics (volume, velocity and variety) into relevant security user profiles. Users’ information will be analyzed to create behavioral profiles. Just-in-time challenging questions are generated by these behavioral profiles, allowing an authentication on demand feature to be obtained. The proposed model assumes that the data is received from different sources. This data is analyzed using collaborative filtering (CF), a learning technique, that builds up knowledge by aggregating the collected users’ transaction data to identify information of security potential. Four use case scenarios were evaluated regarding the proposed model’s proof of concept. Additionally, a web based case study using MovieLens public dataset was implemented. Results show that the proposed model is successful as a proof of concept. The experiment confirms the potential of applying the proposed approach in real life as a new authentication method, leveraging the characteristics of Big data: volume, velocity and variety