RESUMOPredizer novos relacionamentos dentro de um grupo socialé uma tarefa complexa, porém extremamenteútil para potencializar ou maximizar colaborações por meio da indicação de quais seriam as parcerias mais promissoras. Nas redes sociais acadêmicas, a predição de relacionamentosé tipicamente utilizada para tentar identificar potenciais parceiros no desenvolvimento de um projeto e/ou coautores para a publicação de um artigo. Este artigo apresenta um sistema que combina técnicas de inteligência artificial com o estado da arte das métricas de predição de relacionamentos em redes sociais. O sistema resultante foi testado usando dados reais de pesquisadores em Ciência da Computação e atingiu uma precisão superior a 99,5% na predição de novas coautorias. Palavras-Chavepredição de relacionamentos, redes sociais, redes acadêmicas ABSTRACT The prediction of new relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating what the most promising partnerships are. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents a system that combines artificial intelligence techniques with the state-ofthe-art metrics for link prediction. The resulting system was
The computational needs of scientific experiments often require powerful computers. One alternative way to obtain this processing power is taking advantage of the idle processing of personal computers as volunteers. This technique is known as volunteer computing and has great potential in helping scientists. However, there are several issues which can reduce the efficiency of this approach when applied to complex scientific experiments, such as, the ones with long processing time, very large input or output data, etc. In order to face these challenges, we designed a volunteer computing system based on peer-to-peer communication. When compared with the local execution of activities and traditional volunteer computing, the execution time was improved and, in some cases, there was also a reduction of the server upload bandwidth use. Scientific experiments, in several cases, are organized as bag-of-tasks [Kwan and Jogesh 2010] or scientific workflows [Medeiros et al. 1996]. Bag-of-tasks are composed of a set of completely independent tasks, what is very different from workflows where a task needs to wait for the conclusion of another task. Both, typically, require huge computational power and a way to obtain it is the use of several personal computers, for example, desktop grids [Kondo et al. 2007, Anderson 2004] or volunteer computing [Anderson and Fedak 2006]. Volunteer computing (VC) projects take idle resources from donors: the tasks are sent to volunteers (in general using the Internet), and they send the results back to a server. This approach may provide a lot of computational power [Anderson 2004], but in scientific experiments, there are many issues which can turn this approach inefficient, such as long processing tasks [Dethier et al. 2008], great volumes of data to be transferred [Duan et al. 2012] or instability in volunteer computers [Dias et al. 2010]. The majority of these issues are related to the low-speed communication with donors across the Internet. The development of this work was based on an extension [Digiampietri et al. 2014b] of the Workflow Management System (WfMS) called WOODSS (A Spatial Decision Support System based on Workflows) [Seffino et al. 1999], an open source system written in Java extended in this project to deal with P2P communication.
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