Análise de Prazos de Entrega de Atividades no Moodle: um Estudo de Caso Utilizando Mineração de Dados

Este artigo descreve um estudo realizado sobre os dados gerados na interação com o Ambiente Virtual de Aprendizagem (AVA) Moodle em uma instituição de ensino, com foco na análise de prazos e datas efetivas de submissões de tarefas neste ambiente. O principal objetivo do trabalho é obter informações  relevantes sobre a postagem de tarefas no ambiente, para subsidiar ações que possam auxiliar a reduzir o envio de trabalhos após o prazo estipulado ou muito próximo ao final do período de postagem. Para isso, são considerados o período em que a tarefa permaneceu aberta para postagem, o curso proveniente da tarefa e o período em que a postagem foi realizada. Esse estudo foi realizado seguindo as etapas do processo de descoberta de informação, com a utilização de algoritmos de mineração de dados da ferramenta Weka

Ambientes de Desenvolvimento Integrado no Apoio ao Ensino da Linguagem de Programação Haskell

. A linguagem Haskell é comumente utilizada em universidades para ensino do paradigma de programação funcional. Neste artigo, defende-se que o uso de um ambiente de desenvolvimento integrado  (Integrated Development Environment - IDE) é vantajoso para apoiar o ensino e o aprendizado desta linguagem. Nesta linha, apresenta-se primeiramente uma análise comparativa de IDEs disponíveis para Haskell e, em seguida, relata-se uma experiência realizada utilizando-se o IDE Eclipse em sala de aula. Os resultados contribuem para a adoção deste tipo de ferramenta no ensino de Haskell

Cloud computing with Google Apps for education: An experience report

This article presents an experience report on using Google Apps for Education in a computer science laboratory at the Federal University of Santa Maria, Brazil. Google Apps platform offers a range of applications in a SaaS (Software as a Service) cloud, bringing several facilities for members of the institution, but also some challenges for system administrators. Throughout the article, we describe the migration to the cloud platform, the current state of the migrated domain and some opportunities that are being explored to best meet our requirements.Key words: cloud computing, Software as a Service, Google Apps, system administration

PER-MARE: Adaptive Deployment of MapReduce over Pervasive Grids

International audienceMapReduce is a parallel programming paradigm successfully used to perform computations on massive amounts of data, being widely deployed on clusters, grid, and cloud infrastructures. Interestingly, while the emergence of cloud in- frastructures has opened new perspectives, several enterprises hesitate to put sensible data on the cloud and prefer to rely on internal resources. In this paper we introduce the PER- MARE initiative, which aims at proposing scalable techniques to support existent MapReduce data-intensive applications in the context of loosely coupled networks such as pervasive and desktop grids. By relying on the MapReduce programming model, PER-MARE proposes to explore the potential advan- tages of using free unused resources available at enterprises as pervasive grids, alone or in a hybrid environment. This paper presents the main lines that orient the PER-MARE approach and some preliminary results

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

International audienceThis study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids

Análise de Prazos de Entrega de Atividades no Moodle: um Estudo de Caso Utilizando Mineração de Dados

Este artigo descreve um estudo realizado sobre os dados gerados na interação com o Ambiente Virtual de Aprendizagem (AVA) Moodle em uma instituição de ensino, com foco na análise de prazos e datas efetivas de submissões de tarefas neste ambiente. O principal objetivo do trabalho é obter informações  relevantes sobre a postagem de tarefas no ambiente, para subsidiar ações que possam auxiliar a reduzir o envio de trabalhos após o prazo estipulado ou muito próximo ao final do período de postagem. Para isso, são considerados o período em que a tarefa permaneceu aberta para postagem, o curso proveniente da tarefa e o período em que a postagem foi realizada. Esse estudo foi realizado seguindo as etapas do processo de descoberta de informação, com a utilização de algoritmos de mineração de dados da ferramenta Weka

Context-Aware Scheduling for Apache Hadoop over Pervasive Environments

International audienceThis article proposes to improve Apache Hadoop scheduling through the usage of context-awareness. Apache Hadoop is the most popular implementation of the MapReduce paradigm for distributed computing, but its design doesn't adapt automatically to computing nodes' context and capabilities. By introducing context-awareness into Hadoop, we intent to dynamically adapt its scheduling to the execution environment. This is a necessary feature in the context of pervasive grids, which are heterogeneous, dynamic and shared environments. The solution has been incorporated into Hadoop and evaluated through controlled experiments. The experiments demonstrate that context-awareness provides comparative performance gains, especially when part of the resources disappear during execution

Performance Improvement of Data Mining in Weka through GPU Acceleration

http://www.sciencedirect.com/science/article/pii/S1877050914006024International audienceData mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to improve the performance of Weka, a popular data mining tool, through parallelization on GPU-accelerated machines. From the profiling of Weka object-oriented code, we chose to parallelize a matrix multiplication method using state-of-the-art tools. The implementation was merged into Weka so that we could analyze the impact of parallel execution on its performance. The results show a significant speedup on the target parallel architectures, compared to the original, sequential Weka code

Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

International audienceData mining tools may be computationally demanding, which leads to an increasing interest on par- allel computing strategies in order to improve their per- formance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the com- puting power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investi- gates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel exe- cution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates

Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling

International audienc