Big data in cloud: a data architecture

Nowadays, organizations have at their disposal a large volume of data with a wide variety of types. Technology-driven organizations want to capture process and analyze this data at a fast velocity, in order to better understand and manage their customers, their operations and their business processes. As much as data volume and variety increases and as faster analytic results are needed, more demanding is for a data architecture. This data architecture should enable collecting, storing, and analyzing Big Data in Cloud Environment. Cloud Computing, ensures timeliness, ubiquity and easy access by users. This paper proposes to develop a data architecture to support Big Data in Cloud and, finally, validate the architecture with a proof of concept.This work was financed by FCT - Fundação para a Ciência e Tecnologia for the project: PEst-OE/EEI/UI0319/201

A multi-driven approach to requirements analysis of data warehouse model: A case study

In this paper, a multi-driven approach to data modeling in data warehousing will be presented, which integrates three existing approaches normally used separately: goal-driven, user-driven and data-driven; and two approaches usually not used in data warehousing field: process-driven and technology-driven. Goal-driven approach produces subjects and KPI’s (Key Performance Indicators) of main business fields. User-driven approach produces analytical requirements represented by measures and dimensions of each subject. Process-driven approach propose improvements in business processes (by using and creating subject oriented enterprise data model) to satisfy the KPI’s, measures and dimensions identified in the previous approaches. Technology-driven approach is an enabler or an obstacle to be considered in a data warehouse model. Data-driven approach is a combination of the results of previous approaches and results in a data warehouse model. By using a multi-driven approach with five stages, a layered data warehouse model more aligned with business and individual needs can be obtained. This will be illustrated by using examples of a case study

Análise comparativa do sistema nacional de colocação de professores em Portugal

A colocação dos professores no ensino secundário em Portugal é efetuada sobre a responsabilidade do Ministério da Educação e Ciência, que é quem define as regras do funcionamento do sistema, ou seja é um sistema centralizado. Esse sistema é alvo de críticas por parte dos seus intervenientes sobretudo dos próprios professores. Este artigo tem como objetivo efetuar uma revisão sistemática e uma meta análise de como são efetuadas as colocações de professores em Portugal, bem como noutros países europeu

A modern data architecture: a test case

Atualmente os dados são vistos como tendo tipos e origens distintas. Os tipos de dados podem ser estruturados, semiestruturados e não estruturados. As origens dos dados podem ser diversas como Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), folhas de cálculo, documentos de texto, redes sociais, imagens, vídeos, sensores entre outros. Esta diversidade de dados exige uma arquitetura moderna que permita a recolha dos dados de várias origens e tipos, viabilizando igualmente a extração, transformação e limpeza dos mesmos através do processo de Extract, Transform and Load (ETL), bem como o armazenamento e integração dos dados para posteriores análises. Esta arquitetura deve ser suportada por um ambiente de Cloud Computing, garantindo assim a sua atualidade, ubiquidade e fácil acesso pelos utilizadores. Este artigo propõe-se desenvolver uma arquitetura e implementar uma solução que será validade através de um caso de teste com dados da área da saúde.Currently the data are seen as having distinct types and origins. Data types may be structured, semi-structured and unstructured. The data sources can be diverse as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), spreadsheets, text documents, social networks, images, videos, and other sensors. This diversity of data requires a modern architecture that enables the collection of data from multiple sources and types, also enabling the extraction, processing and cleaning of data through the Extract and Transform and Load (ETL) process, and the storage and integration of data for further analysis. This architecture must be supported by a Cloud Computing environment, thus ensuring its relevance, ubiquity and easy access by users. This article proposes to develop an architecture and implement a solution that is validated through a test case with health care data.Fundação para a Ciência e a Tecnologia (FCT) - PEst-OE/EEI/UI0319/201

Information systems development course: integrating business, IT and IS competencies

Information systems development (ISD) is a capstone course in the Information Systems and Technology undergraduate program at School of Engineering, University of Minho, Portugal. ISD is viewed as an organizational change project that aims at improving an organization through the adoption of IT applications. The course is designed following a project led approach. The project involves describing an organization as a system, describing its information handling activities and proposing a set of IT applications that could be adopted and used. Students are guided by a ISD methodology that demands the application of previous developed competencies in areas such as: organization theory, accounting, marketing, information systems fundamentals, data bases, software engineering, computer networks and several other IT courses. Together with the ISD course, students are also taking courses on organizational behavior and data-warehousing. Students are organized into large teams of 10 to 12 members. Several roles are distributed among team members: e.g., team leader, analyst, document officer, technology officer, methodologist, development tools specialist, IT specialist. Students are suggested a fictional organization in a specific business area. Ideally students should deal with a real organization. As the course is having around 100 students enrolled this is not possible. However it is common that each project team finds an organization in the proposed business area where they go and have actual contact with an organization. The main outputs of the project include: project plan; organization description including - purpose, environment, main activities, business ontology, main performance indicators; general information systems description using UML; requirements for an IT application; IT architecture. One of the most important steps of the project is to decide on what IT to suggest to the organization. The decision should take into consideration the capability of current IT, the specifics of the business area and its current practices. Besides the reports, each team makes two public presentations. The first one is to present the organization description making sure business is clearly understood. The final one is to present the solution in terms of information systems and IT architecture. These presentations are attended by industry guests that focus their attention on the students' communication skills from the perspective of a manager. The evaluation of students' performance is based on: reports corresponding to the outputs mentioned above; public presentations; weekly assessments of the teams' progress. The final mark attributed to each team (a numerical value from 0 to 20, where above 10 is a pass) can be re-distributed among team members, by themselves, in order to account for different levels of commitment or effort within the team. Several other rules are set in order to promote professional behavior.(undefined

Information visualization: conceptualizing new paths for filtering and navigate in scientific knowledge objects

More than 6,849.32 new research journal articles are published every day! Who has time to read every article or document that’s relevant to their research? Access to the right and relevant information is paramount for scientific discoveries. Filtering relevant information has become a fundamental challenge in the actual scientific deluge panorama. As information glut grows ever worse, understanding and visualizing the science social behavior may become our only hope for handling a growing deluge of scientific information. It is therefore fundamental to analyze and interactively visualize the science social space. This paper theoretically conceptualizes an approach aimed at the filtering and navigation of relevant Scientific Knowledge Objects (SKOs) based on a symbiosis between different sub-disciplines domains. We present two main contributions, a comparison among several projects with some relevant use of information visualization in scholarly scientific navigation; and an architecture which will be in line with the most recent international standards and good practices for Open Data, especially those related to Linked Open Data capable to perform an innovative information visualization of relevant SKOs. These contributions are relevant to scholarly and to practitioner’s communities and to who want to access and navigate in relevant SKOs.This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Educomunicação e suas áreas de intervenção: Novos paradigmas para o diálogo intercultural

oai:omp.abpeducom.org.br:publicationFormat/1O material aqui divulgado representa, em essência, a contribuição do VII Encontro Brasileiro de Educomunicação ao V Global MIL Week, da UNESCO, ocorrido na ECA/USP, entre 3 e 5 de novembro de 2016. Estamos diante de um conjunto de 104 papers executivos, com uma média de entre 7 e 10 páginas, cada um. Com este rico e abundante material, chegamos ao sétimo e-book publicado pela ABPEducom, em seus seis primeiros anos de existência. A especificidade desta obra é a de trazer as “Áreas de Intervenção” do campo da Educomunicação, colocando-as a serviço de uma meta essencial ao agir educomunicativo: o diálogo intercultural, trabalhado na linha do tema geral do evento internacional: Media and Information Literacy: New Paradigms for Intercultural Dialogue

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost