GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys

We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.

OPTIMIZED ORGANIZATION AND ADAPTIVE VISUALIZATION OF COMPLICATED MOUNTAIN DISASTER 3D SCENES FOR DIVERSE TERMINALS

<p><strong>Abstract.</strong> Mountain disaster scenes usually contains various geographical entities, which are dynamic and complicated. Therefore, the construction of mountain disaster 3D scenes has great significance for disaster simulation, analysis and prediction. 3D visualization and interactive analysis of complicated mountain disaster scenes should accommodate users who have access to various terminals in disaster emergency response. However, most of the existing 3D visualization methods can only deal with 3D scene data organization and scheduling for single terminal or limited kinds of geographical entities. Due to performance constraints, it’s difficult for Mobile devices to support efficient visualization of complicated mountain disaster 3D scenes, either. To address these issues, we research the key technologies for efficient visualization of mountain disasters on diverse terminals. We utilize the B/S architecture and research its impact on rendering frame rate and the relationship of terminal characteristics and parameters through analysis of terminal characteristics and parameters, e.g. hardware performance, screen size and resolution, network environment, and rendering frame rate requirements. Then we analyze the diverse organization of mountain disaster scene data and explore the methods of constructing efficient spatial index by taking into account the characteristics of diverse terminals. An adaptive scene analysis method is subsequently designed to select the optimal model. Finally, based on the diverse organization of various data in the scene, a corresponding dynamic scheduling method is proposed to realize the adaptive visualization of complicated mountain disaster 3D scenes for diverse terminals.</p>