[pt] Dentro do processo de publicação de dados na Web recomenda-se interligar os dados entre diferentes fontes, através de recursos similares que descrevam um domínio em comum. No entanto, com o crescimento do número dos conjuntos de dados publicados na Web de Dados, as tarefas de descoberta e seleção de dados tornam-se cada vez mais complexas. Além disso, a natureza distribuída e interconectada dos dados, fazem com que a sua análise e entendimento sejam muito demorados. Neste sentido, este trabalho visa oferecer uma arquitetura Web para a identificação de fontes de dados em RDF, com o objetivo de prover melhorias nos processos de publicação, interconex ão, e exploração de dados na Linked Open Data. Para tal, nossa abordagem utiliza o modelo de MapReduce sobre o paradigma de computa ção nas nuvens. Assim, podemos efetuar buscas paralelas por palavraschave sobre um índice de dados semânticos existente na Web. Estas buscas permitem identificar fontes candidatas para ligar os dados. Por meio desta abordagem, foi possível integrar diferentes ferramentas da web semântica em um processo de busca para descobrir fontes de dados relevantes, e relacionar tópicos de interesse denidos pelo usuário. Para atingir nosso objetivo foi necessária a indexação e análise de texto para aperfeiçoar a busca de recursos na Linked Open Data. Para mostrar a ecácia de nossa abordagem foi desenvolvido um estudo de caso, utilizando um subconjunto de dados de uma fonte na Linked Open Data, através do seu serviço SPARQL endpoint. Os resultados do nosso trabalho revelam que a geração de estatísticas sobre os dados da fonte é, de fato, um grande diferencial no processo de busca. Estas estatísticas ajudam ao usuário no processo de escolha de indivíduos. Um processo especializado de extração de palavras-chave é aplicado para cada indivíduo com o objetivo de gerar diferentes buscas sobre o índice semântico. Mostramos a escalabilidade de nosso processo de recomendação de fontes RDF através de diferentes amostras de indivíduos. / [en] In the Web publishing process of data it is recommended to link the data from different sources using similar resources that describe a domain in common. However, the growing number of published data sets on the Web have made the data discovery and data selection tasks become increasingly complex. Moreover, the distributed and interconnected nature of the data causes the understanding and analysis to become too prolonged. In this context, this work aims to provide a Web architecture for identifying RDF data sources with the goal of improving the publishing, interconnection, and data exploration processes within the Linked Open Data. Our approach utilizes the MapReduce computing model on top of the cloud computing paradigm. In this manner, we are able to make parallel keyword searches over existing semantic data indexes available on the web. This will allow to identify candidate sources to link the data. Through this approach, it was possible to integrate different semantic web tools and relevant data sources in a search process, and also to relate topics of interest denied by the user. In order to achieve our objectives it was necessary to index and analyze text to improve the search of resources in the Linked Open Data. To show the effectiveness of our approach we developed a case study using a subset of data from a source in the Linked Open Data through its SPARQL endpoint service. The results of our work reveal that the generation and usage of data source s statistics do make a great difference within the search process. These statistics help the user within the choosing individuals process. Furthermore, a specialized keyword extraction process is run for each individual in order to create different search processes using the semantic index. We show the scalability of our RDF recommendation process by sampling several individuals.

Thesis (M. Ed.) -- University of Limpopo, 2019 / The purpose of this study was to investigate the effect of guided discovery instructional strategy on the grade 9 learners’ performance in chemical reactions. Secondly, to determine the effect of guided discovery instructional strategy on gender (boy and girl). The quantitative, descriptive and inferential research was conducted to determine if there were any differences between the performance of learners taught using Guided discovery and learners taught using direct instruction. Data collection was done using pre-test and post-test. Two groups of learners participated in the study. The experimental group (n = 40) was taught through Guided discovery. The second was Control Group (n = 35) taught through direct instruction. The findings reveal that guided discovery instructional strategy resulted in better performance of learners in science than direct instruction. Learners expressed an increased interest, motivation and self-efficacy after being exposed to guided discovery. Therefore, the study recommends that teachers need to move learners from dependent direct instruction to more independent learning through guided instruction. KEY TERMS Guided discovery learning, performance, learner

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East Tennessee State University recently adopted Alma & Primo and was suprised by the lack of an A-Z database discovery module. Frustrated by having to maintain electronic resources separately on our library website and in Alma, we embarked on a goal to eliminate redundancy and use Alma/Primo exclusively. This presentation will cover our entire workflow in both Alma & Primo and the issues we encountered along the way. I'll first go over our process in Alma including MARC record creation, electronic collection setup, and the top level collection module. Next, I'll cover our workflow in Primo including normalization rules, scoping, PNX display, facets, and code table changes. The last section will cover the Primo X-Services API and how it was developed into an A-Z Database list.

Are we doing enough to market newly acquired book titles? Libraries purchase and subscribe to many new book titles each year, both print and electronic. However, we rely on the expectation that users will periodically search our systems to discover newly acquired titles. Static lists and displays have been traditional marketing methods for libraries, but require tedious time and effort to maintain. Without a practical solution for an academic library, East Tennessee State University developed an automated process to generate book widgets utilizing data from Alma Analytics. These widgets are now deployed in our subject guides, website, and on our digital displays. This article outlines the development and implementation of these widgets. We also discuss the challenges we encountered, such as finding image covers and custom subject tagging.