Artigos, teses e dissertações: o que é mais consultado?

Eu sempre achava que teses e dissertações só seriam lidas pela banca e por uns poucos pesquisadores relacionados com o projeto. Há um ano, mais ou menos, resolvi dar uma olhada nos acessos às teses de meus ex-orientandos no sistema da UFRGS e tive um grande surpresa! Olhem os gráficos a seguir:

O primeiro histograma é uma dissertação de mestrado, o segundo e terceiro são teses de doutorado, mesmo a mais recente já tem um número expressivo de acessos.

Conclusão: Muito cuidado e esforço para redigir uma boa tese ou dissertação!

As pessoas já entenderam que a bibliografia naïf que conta o número de artigos e não a qualidade intrínseca do trabalho não estimula a leitura de artigos bem fatiados, preferem buscar a qualidade do trabalho. Um texto completo de uma tese ou dissertação é muito melhor, tem qualidade intrínseca baseada na apresentação completa do trabalho de pesquisa. Eu gostaria que um aluno meu de doutorado publicasse UM artigo de alta qualidade e não uns 5 ou seis com QUALIS do índice restrito, mas cada um apresentando uma pequena faceta do seu trabalho.

 

Novo artigo: An Interface Prototype Proposal to a Semiautomatic Process Model Verification Method Based on Process Modeling Guidelines

Cite this paper as:
Júnior V.H.G., Dani V.S., Avila D.T., Thom L.H., de Oliveira J.P.M., Fantinato M. (2018) An Interface Prototype Proposal to a Semiautomatic Process Model Verification Method Based on Process Modeling Guidelines. In: Hammoudi S., Śmiałek M., Camp O., Filipe J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham

DOI https://doi.org/10.1007/978-3-319-93375-7_28
Publisher NameSpringer, Cham
Print ISBN 978-3-319-93374-0
Online ISBN 
978-3-319-93375-7

Abstract

The design of comprehensible process models is a very complex task. In order to obtain them, process analysts usually rely on process modeling guidelines. This is specially true when dealing with collections counting up to hundreds of process models, since querying or organizing such a collection is not easy. In this paper we report a method presented in an earlier work to verify if a process model is following process modeling guidelines. In addition we propose an interface prototype to display which process models are not following which guidelines. A collection of 31 process models were used to validate the identification method and the results shows that 23 of these process models contains at least one guideline violation.

Keywords

Business process, BPMN, Ontology, Process model quality, Modeling guidelines, Information visualization


 

Novo artigo: BEATnIk: an algorithm to Automatic generation of educational description of movies

Logo CBIE 2017

O artigo recebeu Menção Honrosa no Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação – SBIE 2017)


Vinicius Woloszyn, Guilherme Medeiros Machado, José Palazzo Moreira de Oliveira, Leandro Wives, Horacio Saggion

Resumo

Teachers and professors have increasingly employed different methods to enrich the learning of a subject in class, drive other assignments, and meet curriculum standards, are some examples of it. One of such methods is the use of movies as an alternative educational experience to support class discussions. In this sense, websites such as www.TeachWithMovies.com, arise as a valuable support to the creation of lesson plans. In this website, each movie is described as a lesson plan targeting the learn of a subject, the plans comprise the movie benefits and possible problems, a helpful background, the discussion and some interesting questions to be presented to the students. However, the creation of such lesson plan or even a simple educational description of the movie can demand much work and time, since the developed text must consider educational aspects of the movie. In this paper, we describe BEATnIk (Biased Educational Automatic Text Summarization) an algorithm to automatic generation of movie’s summaries, such algorithm favors educational aspects from the text to generate a biased educational summary. The algorithm input, are users’ comments about the each movie that has a lesson plan in the TeachWithMovies (TWM) website, and the user’s comments extracted from amazon’s website. BEATnIk constructs a complete graph for each movie where each sentence from the comments’ set becomes a node, and each edge weight is defined by the value of an adapted cosine similarity between the sentences. The algorithm then employs the PageRank to compute the centrality of each node. The intuition behind this approach is that central sentences highlight aspects of a movie that many other reviews frequently mention. In addition, BEATnIk takes into account keywords extracted from the lesson plans of TWM. The final educational summary is based on the centrality score of the sentences pondered by the presence of educational keywords. The comparison of our approach to TextRank, which is a Graph-based Automatic Text Summarization, revealed that BEATnIk generate summaries closer to the description of the movies in TWM. The experiments showed that our approach statistically outperforms the baseline in precision, and achieves better results both in recall and f-score using ROUGE-n, which is a set of metrics used for evaluating automatic summarization.


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