Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm (AAA) based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms.
Several algorithms have been proposed in the last years for discovering stops in trajectories of moving objects. Some methods consider as stops the subtrajectories that i) have speed lower than the average trajectory speed, ii) present significant direction changes, iii) have gaps, or iv) intersect a given spatial region. In these approaches a time constraint should be met for the subtrajectory to be considered as a stop, and this constraint is absolute (it is met or not). Indeed, these approaches consider stops as a continuous subtrajectory. In this paper, we show that for several application domains the stops do not need to be continuous, and the time constraint should be relaxed. In summary, we present the definitions of non-continuous stops and present an algorithm to discover a new kind of stops. We evaluate the proposed algorithm with a running example and real trajectory data, comparing it to the most similar approach in the literature, the SMoT algorithm.
<p>La creciente y enorme cantidad de datos, del orden de exabytes, generados por las aplicaciones empresariales actuales han originado conjuntos masivos de estos. Los sistemas de gestión de bases de datos (SGBD) NoSQL han surgido como una alternativa a los SGBD relacionales para la gestión de estos conjuntos. Entre los principales SGBD NoSQL está MongoDB. En este artículo se compara el rendimiento entre MongoDB y Oracle (uno de los principales SGBD que soporta bases de datos relacionales). La comparación se basa en las operaciones de inserción, consulta, actualización y borrado (CRUD, por sus siglas en inglés). Aunque se requieren experimentos más exhaustivos y muchos otros tipos de pruebas, los resultados ofrecen un punto de partida para el análisis de rendimiento en estos SGBD.</p>
The fields of finance and accounting are especially susceptible to fraud. For this reason, special techniques have been developed for prevention or detection in these fields. For instance, in the field of finance, there are proposals focused on historical data analysis and statistical distribution, credit card transaction analysis and financial statements. 1 2 3 One of them is an integrated language model. 4 The fields of finance and accounting are especially susceptible to fraud. For this reason, special techniques have been developed for prevention or detection. Francisco Javier Moreno Arboleda, Jaime Alberto Guzman-Luna and Ingrid-Durley Torres present several fraud detection techniques, focusing on the fields of finance and forensic accounting. Based on these techniques, they define specialised operators focusing on fraud detection in a data warehouse. While the techniques are able to flag potential frauds, there is some way to go to being able to positively identify them, and the authors suggest future work in this area.
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