Asdrúbal López‐Chau

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.

Automatic face recognition has evolved significantly with the use of deep learning techniques, enabling more robust systems that can withstand variations in lighting, pose, and expression. This paper presents the implementation of a face recognition system for video recordings using the MTCNN face detection algorithm, which is used for its accuracy in identifying and locating faces in uncontrolled environments with varying lighting conditions. After face detection, the VGG-16 model was used as a deep feature extractor, leveraging its architecture to employ transfer learning techniques to learn hierarchical and discriminative representations of the face. The system processes the video frames using the OpenCV library, then uses the MTCNN network for facial identification, and finally assigns identities through a classifier trained, as previously mentioned, with the VGG-16 model. In recent studies, authors report that convolutional neural networks consistently outperform traditional methods due to their ability to automate the extraction of relevant features and handle complex environmental conditions. Preliminary results indicate that the MTCNN–VGG-16 integration forms an effective strategy for facial recognition in real-world scenarios, demonstrating high accuracy and stability under diverse visual conditions. This work provides an accessible and reproducible architecture for potential applications in video analytics, surveillance, and intelligent automation

La clasificación en conjuntos de datos no balanceados es un problema actual e \nimportante (Khan2012). Ejemplos de aplicaciones del mundo real en las que se \ngeneran este tipo de datos son, por ejemplo, diagnósticos médicos de \nenfermedades tales como cáncer o diabetes mellitus, detección de errores en \ncódigo fuente, ataques informáticos en servidores e identificación de transacciones \nbancarias fraudulentas. En este tipo de escenarios, es sumamente importante \ndetectar casos que generalmente ocurren con poca frecuencia con respecto a la \ncantidad de datos obtenidos, ya que representan un riesgo que hay que observar \noportunamente. Para los ejemplos de aplicaciones mencionadas anteriormente, \nestos casos poco frecuentes serían los pacientes enfermos de cáncer o diabetes, \nlos fragmentos de código fuente con errores, los accesos inválidos a servidores y \nlas transacciones ilícitas. Sin embargo, como se mencionó anteriormente, los \nalgoritmos de clasificación, presentan problemas con conjuntos de datos no \nbalanceados (He2009).