SILKNOWViz: Spatio-temporal data ontology viewer

Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods

Disentangling Electronic and Geometric Effects in Electrocatalysis through Substitution in Isostructural Intermetallic Compounds

Efficient development of catalytic materials requires knowledge of the decisive parameters defining the catalytic properties. In multicomponent metallic catalysts, these are categorized as electronic and geometric effects, yet they are strongly interrelated. A systematic disentanglement can be achieved by fixing one parameter while altering the other, which becomes possible through the substitution in isostructural intermetallic compounds. This approach enables the evaluation of electronic or geometric contributions both individually and combined. Herein, this is achieved by substitution of indium (three valence electrons) with tin (four valence electrons) in the series In(1-x)SnxPd(2), which allows for a systematic variation of the total number of electrons per unit cell with only a minor variation of the unit cell parameters and thus the evaluation of the electronic effect. Geometric effects were evaluated by substitution of indium with gallium in the Ga(1-x)InxPd(2) series, which allows for a systematic variation of the interatomic distances while maintaining the same number of valence electrons per unit cell and close atomic coordinates. By substituting gallium with tin in the Ga(1-x)SnxPd(2) series, both effects are combined and addressed simultaneously. The activity enhancement of the methanol oxidation reaction on the Ga(1-x)SnxPd(2) series is attributed to the synergy of the combined effects

Image pattern recognition in big data: taxonomy and open challenges: survey

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research