This article discusses the information representation process based on the Moscovici's Social Representation Theory and domain analysis in Information Science. The aim was to identify mechanisms and constituent dimensions of social representation in collaborative tagging systems/social bookmarking systems. Scientific knowledge was defined as the object/phenomenon of representation in these systems; and the tag as the shareable structure of meaning that connects participants and resources. The empirical research involved descriptive statistical techniques applied to a corpora of tags available in CiteULike, which is a social tagging system developed for the academic community. The data analysis, performed in a sample of groups derived from the dataset, showed that the users' reuse of their own tags resembles the anchorage mechanism. The reuse of tags by other participants -in the same group -reveals some evidence of the objectification mechanism. Some speculation arose about the cognitive effort made by the individual, under group influence, with regard to the tagging activity, user's choice of resources, and sharing styles. Further studies on social bookmarking systems depend both on a "gain scale" of users and items tagged, requiring techniques and procedures redesigned by Information Science, Statistics, Network Analysis, Linguistics/Sociolinguistics and Social Psychology. Resumo O artigo discute um recorte na temática de representação da informação explorando a Teoria das Representações Sociais e a abordagem da análise de domínio da Ciência da Informação. Teve como objetivo geral identificar mecanismos e dimensões constituintes da representação social em grupos de participantes de sistemas de colaborativos de marcação (ou sistemas de marcação social). Definiu--se o conhecimento científico como objeto/fenômeno de representação em tais sistemas; delimitou-se a tag/marcação como unidade de registro e de significado; e se considerou os usuários, o conjunto total de tags, e os itens marcados como unidade de contexto. A pesquisa empírica envolveu técnicas de estatística descritiva aplicada à corpora de tags disponíveis em datasets do
Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA -Memetic Algorithm-based Highly Adapted Tool for Motif Ascertainment -a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on Genetic Programming using operators specially tailored for the target problem. The final performance was measured using sensitivity (Se), specificity (Sp) and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.
Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish the common biological functions. This paper presents a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing the primary structure. Runnings were done with the enzymes subset extracted from the Protein Data Bank. The heuristic method used was based on a genetic algorithm using specially tailored operators for the problem. Motifs found were used to build a decision tree using the C4.5 algorithm. The results were compared with motifs found by MEME, a freely available web tool. Another comparison was made with classification results of other two systems: a neural network-based tool and a hidden Markov model-based tool. The final performance was measured using sensitivity (Se) and specificity (Sp): similar results were obtained for the proposed tool (78.79 and 95.82) and the neural network-based tool (74.65 and 94.80, respectively), while MEME and HM-MER resulted in an inferior performance. The proposed system has the advantage of giving comprehensible rules when compared with the other approaches. These results obtained for the enzyme dataset suggest that the evolutionary computation method proposed is very efficient to find patterns for protein classification.
Recognized as a public health issue, self-medication is employed for pain ceasing and disease prevention, or even for aesthetic purposes, although it can compromise a person's health. Part of the information that leads to self-medication is disseminated via the Internet, in online discussion groups like the ones of Facebook, the major social media platform in the world and in Brazil. This paperwork aims at analyzing if, within such groups, there are incentives to self-medication for weight loss, and how such information could be classified for better understanding. The study is a descriptive survey, with a non-experimental approach. For data treatment and analysis is employed the Descending Hierarchical Classification (DHC or Reinert's Method), using IRaMuTeQ software. Results encompass the identification of classes of weight loss-related themes, which can contribute to self-medication with said intent. It is possible to apply methods of this study to other social media sites and other platforms of interaction throughout the Internet.
Este artigo apresenta um estudo de caso de aplicação do KDD (Knowledge Discovery in Databases) sobre Ordens de Serviço (OS) de informática de uma instituição hospitalar do Paraná, objetivando identificar novas práticas a serem aplicadas na gestão. Esta pesquisa surgiu da necessidade de conhecer os padrões de relacionamento entre os diversos setores do hospital com a área de informática, no que se refere à solicitação de atendimentos a problemas de Tecnologia de Informação (TI). Dessa forma, seria possível avaliar a atuação efetiva do setor, bem como identificar eventuais desvios ou não conformidades. Para os experimentos foi utilizada a tarefa de classificação, sendo avaliados três métodos, possibilitando a obtenção de regras com potencial de validação por especialista. O resultado produzido neste estudo demonstrou a aplicabilidade do KDD na obtenção de conhecimento relevante para auxílio à tomada de decisão com vistas ao investimento em melhores práticas para ganho de qualidade dos serviços de TI. Dentre eles, destacam-se: o desenvolvimento de planos de ação para ajustes de inconsistências no uso da OS pelos setores para demanda de ações de TI; o estabelecimento de novos parâmetros de priorização dos atendimentos emergenciais dos setores demandantes; o ajuste, implementação e divulgação de novos de procedimentos de atendimento das OS; e em consequência o ganho de agilidade e qualidade no atendimento e a melhora no relacionamento da TI com os demais setores. Palavras-chaves: mineração de dados, gestão hospitalar, governança de tecnologia de informação. PATTERN DISCOVERY IN IT SERVICE ORDERS FOR HOSPITALS ABSTRACTThe article presents a case study about KDD (Knowledge Discovery in Databases) application in IT (information technology) Service Orders (OS) in a hospital in the State of Paraná, aiming to identify new practices to be applied in IT management. The research arose from the need to identify relationship patterns between hospital departments and IT, based on their IT problem demands. From these patterns it could be possible to assess the effective performance of the sector and identify any deviations or non-conformities. The experiment used classification and evaluated three methods, which made it possible to obtain rules capable of expert validation. The results produced in this study demonstrated the KDD applicability to obtain relevant knowledge to aid decision-making for investment in the betterment of IT services. Among them: the development of action plans for adjustment of inconsistencies in IT-managed OS requests; the establishment of new prioritization standards for emergency requests; the improvement, implementation and dissemination of new OS work procedures; and, as a consequence, a increase in agility and service quality and an improvement th IT relationship with other hospital sectors.
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