Avaliação do Balanced Scorecard (BSC) para monitorar o desempenho de empresas operadoras de ônibus urbano o caso da Citral

O processo de desenvolvimento econômico e o modelo de transporte urbano têm agravado as condições de circulação nas cidades, provocando grandes deseconomias e comprometendo a qualidade de vida. A realidade mercadológica do mundo tem mudado o relacionamento das organizações com os seus clientes, principalmente em função da globalização, em vista deste cenário justificam sistemas de avaliação de desempenho de organizações que prestam este serviço público. Este trabalho apresenta uma abordagem para avaliação de desempenho de empresas operadoras de ônibus urbano, encontrou-se a oportunidade de implementar um modelo de avaliação de desempenho e gestão que poderá reorientar as atividades da organização, considerando outras variáveis além das financeiras, como operacionais, do ambiente interno, dos clientes e da responsabilidade social da organização. O caso foi estudado a partir de seis modelos de avaliação de desempenho e gestão, com a opção por um deles. A escolha do modelo Balanced Scorecard (KAPLAN; NORTON, 2001) está vinculada aos objetivos do estudo, que são voltados à análise crítica dos indicadores de desempenho e a conseqüente proposição de um modelo capaz de corrigir desvios e melhorar o desempenho ao longo prazo. O modelo é composto por quatro perspectivas com seus respectivos objetivos estratégicos, vinculados aos fatores críticos de sucesso e foi adaptado da obra Organização voltada para a estratégia dos autores Kaplan e Norton (2001). O modelo proposto foi aplicado numa empresa operadora de ônibus urbano da grande Porto Alegre durante dois ciclos de implantação nos quais obteve-se resultados significativos, que são aqui discutidos as vantagens e desvantagens de sua utilização.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

Ontology-based metadata for e-learning content

Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.