Aquarization: an introduction to the Big Data society

Este artigo abre um caminho para a análise das consequências sociais que advêm das utilizações de Big Data. Os mecanismos e as estratégias de extração, análise e utilização dos dados dos indivíduos convergem numa nova arquitetura e mecanismo de poder que se diagramou num Aquário. O dispositivo desta nova figura de tecnologia política e económica é transparente, automatizado e antecipatório. A tendência é clara, cada vez mais, serviços públicos e privados estão a ser aquarizados.This article opens a way to an analysis of the social consequences of Big Data uses. The mechanisms and strategies for extracting, analyzing and using data from individuals converge in a new architecture and mechanism of power that is diagrammed in an Aquarium. The device of this new figure of political and economic technology is transparent, automated and anticipatory. The trend is clear, more and more public and private services are being aquarized.info:eu-repo/semantics/publishedVersio

Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

Big data and data science have experienced unprecedented growth in recent years.  The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial.  Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses

Exploring the Applicability of Test Driven Development in the Big Data Domain

Big data analytics and the according applications have gained huge importance in daily life. This results on the one hand from their versatility and on the other hand from their capability to greatly improve an organization’s performance when utilized appropriately. However, despite their prevalence and the corresponding attention through practitioners as well as the scientific world, the actual implementation still remains a challenging task. Therefore, without the adequate testing, the reliability of the systems and thus the obtained outputs is uncertain. This might reduce their utilization, or even worse, lead to a diminished decision-making quality. The publication at hand explores the adoption of test driven development as a potential approach for addressing this issue. Subsequently, using the design science research methodology, a microservice-based test driven development concept for big data (MBTDD-BD) is proposed. In the end, possible avenues for future research endeavours are indicated

Utilização painéis interativos na gestão de pequenas empresas

Aguiar, H. e Gouveia, L. (2021). Utilização painéis interativos na gestão de pequenas empresas. Seminário Doutoramento em Ciência da Informação, especialidade Sistemas, Tecnologias e Gestão da Informação (SITEGI). 9 de Julho. Universidade Fernando Pessoa. Porto.info:eu-repo/semantics/publishedVersio

Comparison of time-oriented methods to check manufacturing activities and an examination of their efficiency

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Big Data and Television Broadcasting. A Critical Reflection on Big Data’s Surge to Be-come a New Techno-Economic Paradigm and its Impacts on the Concept of the «Ad-dressable Audience»

The paper explores how big data creates challenges and opportunities to enhance value relationships be-tween TV broadcasters, audiences and advertisers in digital television broadcasting. It finds that research into big data requires much closer attention to critical issues in the social and cultural sciences – with a fo-cus on media and communication studies and its subfield media management – to inspire our understand-ing that big data would perfectly fit the dominant «techno-economic paradigm», a meta-narrative for a substantial technological revolution that has the power to bring about a transformation across the board in ways that when new technologies diffuse, they multiply their impact across the economy and eventually modifies the socio-institutional structures. While asking how big data adds value to a broadcaster’s decision on corporate strategies in Big-Data driven TV is legitimate and important, we remain skeptical as to what effectively is to be gleaned from big data in broadcast TV This is because the socio-cultural dimensions are greatly unresolved. Notably, the corporate strategies of the «addressable audience» or audience com-modification, whereby audiences are effectively sold as mere datacommodities to broadcasters and adver-tisers, must be observed critically.Este artículo explora el estado de la cuestión sobre los desafíos y oportunidades del Big Data para incre-mentar el valor de las relaciones entre los operadores de televisión, las audiencias y los anunciantes que permiten los servicios digitalizados de televisión. Se plantea que la investigación sobre Big Data requiere prestar mayor atención a cuestiones críticas en las ciencias sociales y en la cultura –relacionadas con la comunicación y la gestión de medios– para ayudarnos a comprender que el Big Data puede, perfectamen-te, encajar en el paradigma tecno-económico dominante; una meta-narrativa sobre una revolución tecno-lógica sustancial que tiene el poder de transformar todos los ámbitos: cuando se difunde, multiplica su im-pacto en la economía y, finalmente, modifica las estructuras sociales e institucionales. Aunque es legítimo e importante preguntarse cómo el Big Data proporciona valor a las decisiones estratégicas de los operado-res de televisión, conviene mantener el escepticismo sobre lo que se puede obtener del Big Data para los servicios de televisión mientras las cuestiones socio-culturales no se resuelvan. Hay que analizar con senti-do crítico las estrategias de mercantilización de la audiencia o de target de audiencia, mediante las que sus datos se venden como una simple mercancía a los operadores y anunciantes

Assessing learners’ satisfaction in collaborative online courses through a big data approach

none4noMonitoring learners' satisfaction (LS) is a vital action for collecting precious information and design valuable online collaborative learning (CL) experiences. Today's CL platforms allow students for performing many online activities, thus generating a huge mass of data that can be processed to provide insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to provide valuable information that may improve the CL experience. Besides, the adoption of Big Data offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or surveys). Although the application of Big Data in the CL domain is a recent explored research area with limited applications, it may have an important role in the future of online education. By adopting the design science research methodology, this article describes a novel method and approach to analyse individual students' contributions in online learning activities and assess the level of their satisfaction towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big Data context, with the goal to provide in real-time valuable insights that people and systems can use to intervene properly in the program. The contribution of this paper can be of value for both researchers and practitioners: the former can be interested in the approach and method used for LS assessment; the latter can find of interest the system implemented and how it has been tested in a real online course.openElia G.; Solazzo G.; Lorenzo G.; Passiante G.Elia, G.; Solazzo, G.; Lorenzo, G.; Passiante, G

Ecosistema Big Data en un clúster de Raspberry Pi

Esta investigación mostrara un paso a paso de como instalar y configurar Hadoop en un clúster de raspberrys pi, describiendo y explicando desde los fundamentos de Big Data hasta todo el ecosistema de Apache y para que funciona cada tecnología. Además de recopilar información de algunas de las publicaciones mas relevantes relacionadas con Big Data

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

ALGORITMO VFI: UN ESTUDIO EXPERIMENTAL EN WEKA

Las grandes bases de datos son un reto hoy en día, ya que existe la necesidad de algoritmos de procesamiento más rápidos y confiables. Cuando se utilizan técnicas de aprendizaje automatizado, a menudo involucra un alto costo computacional asociado con el tiempo de entrenamiento; pero no es necesario un nuevo algoritmo si se selecciona el apropiado. Por esta razón, el presente artículo se propone como objetivo: realizar un estudio experimental para comparar un algoritmo conocido y simple llamado Voting Feature Intervals (VFI), con otros influyentes clasificadores, con base en la precisión de la clasificación. La experimentación se llevó a cabo mediante la herramienta WEKA, y se utilizó la metodología estadística de Demšar para evaluar los resultados. Finalmente, se mostró que su comportamiento, en cuanto a la correctitud de la clasificación, no es significativamente peor que otros algoritmos bien conocidos, mientras que su entrenamiento y tiempo de clasificación es lo suficientemente rápido en grandes bases de datos