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- Ponencia en CongresoModels and query languages for temporal property graph database(2022)"Although property graphs are increasingly being studied by the research community, most authors do not consider the evolution of such graphs over time. However, this is needed to capture a wide range of real-world situations, where changes normally occur. In this work, we propose a temporal model and a high level query language for property graphs and analyse the real-world cases where they can be useful, with focus on transportation networks (like road and river networks) equipped with sensors that measure different variables over time. Many kinds of interesting paths arise in this scenario. To efficiently compute these paths, also path indexing techniques must be studied."
- Ponencia en CongresoModeling and querying sensor networks using temporal graph databases(2022)"Transportation networks (e.g., river systems or road net works) equipped with sensors that collect data for several different pur poses can be naturally modeled using graph databases. However, since networks can change over time, to represent these changes appropriately, a temporal graph data model is required. In this paper, we show that sensor-equipped transportation networks can be represented and queried using temporal graph databases and query languages. For this, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph. We redefine temporal paths and study and implement a new kind of path, called Flow path. We take the Flanders’ river system as a use case."
- Ponencia en CongresoIndexing continuous paths in temporal graphs(2022)"Temporal property graph databases track the evolution over time of nodes, properties, and edges in graphs. Computing temporal paths in these graphs is hard. In this paper we focus on indexing Continuous Paths, defined as paths that exist continuously during a certain time interval. We propose an index structure called TGIndex where index nodes are defined as nodes in the graph database. Two different indexing strategies are studied. We show how the index is used for querying and also present different search strategies, that are compared and analyzed using a large synthetic graph."
- Ponencia en CongresoIncorporating coverage criteria in bounded exhaustive black box test generation of structural inputs(2011)"The automated generation of test cases for heap allocated, complex, structures is particularly difficult. Various state of the art tools tackle this problem by bounded exhaustive exploration of potential test cases, using constraint solving mechanisms based on techniques such as search, model checking, symbolic execution and combinations of these. In this article we present a technique for improving the bounded ex haustive constraint based test case generation of structurally complex inputs, for “filtering” approaches. The technique works by guiding the search considering a given black box test criterion. Such a test criterion is incorporated in the constraint based mechanism so that the exploration of potential test cases can be pruned without missing coverable classes of inputs, corresponding to the test criterion. We present the technique, together with some case studies illustrating its performance for some black box testing criteria. The experimental results associated with these case studies are shown in the context of Korat, a state of the art tool for constraint based test case generation, but the approach is applicable in other contexts using a filtering approach to test generation."
- Ponencia en CongresoClasificación de Imágenes SAR utilizando descriptores de textura(2021-10)"Las imágenes SAR (Sythetic Aperture Radar) y PolSAR (Polarimetric Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al méetodo de captura llamado ruido speckle que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de imagen en presencia de ruido speckle y permitan su interpretación automática. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada pixel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales resultan altamente prometedores para aplicarse en imágenes reales."