A grande recessão brasileira : diagnóstico e uma agenda de política econômica

Este artigo tem por objetivo discutir as causas da grande recessão da economia brasileira (2014-2016), bem como apresentar uma agenda de política econômica com vistas à superação dessa crise e retomada do crescimento em bases sustentadas. Argumenta-se que a grande recessão foi causada pela queda acentuada dos gastos de investimento ao longo do ano 2014, cuja origem se encontra na redução das margens de lucro das empresas não financeiras decorrente da elevação do custo unitário do trabalho e da sobrevalorização da taxa de câmbio. Os efeitos recessivos do colapso do investimento foram amplificados pelo realinhamento brusco dos preços relativos ocorrido no primeiro semestre de 2015, e pela contração fiscal executada pelo Ministério da Fazenda no primeiro ano do segundo mandato da presidente Dilma Rousseff. A realização de uma contração fiscal no meio de uma recessão foi, contudo, decorrência da "perda de espaço fiscal" causada pela tendência de redução do resultado primário estrutural observada desde 2007. A superação da atual crise econômica exige a recuperação das margens de lucro das empresas não financeiras, o que requer uma mudança na regra de reajuste do salário mínimo, a continuidade da flexibilização da política monetária e a adoção de um imposto de exportação de commodities. Também é necessário eliminar o caráter pró-cíclico da política fiscal por intermédio da introdução de metas para o resultado primário estrutural.The aim of the present article is to discuss the causes of the great recession of the Brazilian economy (2014-2016), as well as to present an economic policy agenda to restore sustained economic growth. It is argued that the great recession was caused by a huge reduction of investment expenditures in 2014, derived from lower profit margins of nonfinancial companies caused by an increase in unit labor costs and an overvalued exchange rate. The recessionary effects of investment collapse were amplified by the sudden realignment of relative prices on the first semester of 2015, and also by the fiscal contraction that the Ministry of Finance carried out in the first year of President Dilma Rousseff's second mandate. The occurrence of a fiscal contraction amidst a severe recession was due to the "evaporation of fiscal space" caused by the long-term trend of lower primary surplus observed since 2007. Overcoming the current crisis requires an increase in profit margins, which in turn requires a change in the minimum wage laws, the continuation of monetary policy easing and the adoption of an export tax on commodities. It is also necessary to eliminate the procyclical feature of Brazilian fiscal policy by introducing targets for structural primary surplus

From NOSQL databases to decision support systems: developing a business intelligence solution

We are living a time in which the data generated by humans and machines has reached levels never seen before the so-called era of Big Data. Everyday, vast amounts of data, coming from different sources and with different formats, are created and made available to organizations. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations need to explore all the richness that is present in those data. Indeed, data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence (BI). In this paper, we describe the development of a decision support system in which data obtained from a NoSQL database is used to feed a BI solution.This work has been supported by FCT - Fundação para a Ciência e Tecnologia, within the Project Scope: UID/CEC/00319/2019

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution

Educational Process Mining based on Moodle courses: a review of literature

With the prevalence of E-Learning, it is important to analyze how students progress in this environment. These systems collect data about the students’ learning path, and Process Mining (PM) can provide a detailed model of this path. Based on the analysis of ten Educational Process Mining (EPM) case studies involving Moodle event logs, this article aims to contribute a literature review on EPM’s research. Beyond a theoretical introduction to PM and its implications for educational data, the review concludes on what PM tools and techniques are used, as well as the challenges faced in practice. The technical options include software, process discovery algorithms and representation models. These results aim to create a list of available options for future EPM endeavors, in addition to a list of issues to consider in future research involving Moodle

User recognition in AAL environments

Springer - Series Advances in Intelligent and Soft Computing, vol. 72Healthcare projects that intend to decrease the economical and social costs of the real ageing population phenomenon, through the de-localisation of healthcare services delivery and management to the home, have been arising in the scientific community. The VirtualECare project is one of those, so called, Ambient Assisted Living environments, which we have taken a step forward with the introduction of proactive techniques for better adapting to its users, namely elderly or chronic patients, once it is able to learn with their interaction based in contexts. This learning, however, causes the system need to know with whom it is interacting. Basic detection techniques based in possible devices that users carries along with them (e.g. RFID tags, mobile phones, ...) are not good enough, since they can lose/forgot/switch them. To obtain the expected results the technology used has to be more advanced and available in several platforms. One possible and already fairly developed technique is Facial Recognition, and it appears to be the most appropriate one to handle the problem.This document exposes the initial approach of the VirtualECare project to the Facial Recognition area

Macroeconomic Determinants of Bank Spread in Brazil: An Empirical Evaluation

Despite a decline in interest rates since mid-1999, bank spread in Brazil continues extremely high in international terms and in recent years has stood at around 40%. This paper analyses the determinants of bank spread in Brazil, seeking particularly to analyse the macroeconomic determinants of spread in recent times. It uses a VAR model to identify the macroeconomic variables that may directly or indirectly have been influencing spread in Brazil over the period 1994-2005. It presents evidence that interest rate levels and, to a lesser degree, the inflation rate are the main macroeconomic determinants of high bank spread in Brazil.Bank Spread, VAR models, Brazilian banking sector

Decision support in Big Data contexts: a business intelligence solution

In the last few years we all have witnessed an enormous growth in the production of data. According to some estimates, ninety percent of the existing world’s data was created over the past two years! Indeed, we are in the era of Big Data which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of smart devices, mobile applications, cloud computing, and social networks and, more recently, with the advent of the Internet of Things (IoT), data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data, which is the main purpose of Business Intelligence. In this paper we continue the presentation of an experiment in which data obtained from a NoSQL database (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.info:eu-repo/semantics/publishedVersio