Simulation and Visualization of Thermal Metaphor in a Virtual Environment for Thermal Building Assessment

La référence est présente sur HAL mais est incomplète (il manque les co-auteurs et le fichier pdf).The current application of the design process through energy efficiency in virtual reality (VR) systems is limited mostly to building performance predictions, as the issue of the data formats and the workflow used for 3D modeling, thermal calculation and VR visualization. The importance of energy efficiency and integration of advances in building design and VR technology have lead this research to focus on thermal simulation results visualized in a virtual environment to optimize building design, particularly concerning heritage buildings. The emphasis is on the representation of thermal data of a room simulated in a virtual environment (VE) in order to improve the ways in which thermal analysis data are presented to the building stakeholder, with the aim of increasing accuracy and efficiency. The approach is to present more immersive thermal simulation and to project the calculation results in projective displays particularly in Immersion room (CAVE-like). The main idea concerning the experiment is to provide an instrument of visualization and interaction concerning the thermal conditions in a virtual building. Thus the user can immerge, interact, and perceive the impact of the modifications generated by the system, regarding the thermal simulation results. The research has demonstrated it is possible to improve the representation and interpretation of building performance data, particularly for thermal results using visualization techniques.Direktorat Riset dan Pengabdian Masyarakat (DRPM) Universitas Indonesia Research Grant No. 2191/H2.R12/HKP.05.00/201

The Next Generation of the Montage Image Mosaic Toolkit

The scientific computing landscape has evolved dramatically in the past few years, with new schemes for organizing and storing data that reflect the growth in size and complexity of astronomical data sets. In response to this changing landscape, we are, over the next two years, deploying the next generation of the Montage toolkit ([ascl:1010.036]). The first release (October 2015) supports multi-dimensional data sets ("data cubes"), and insertion of XMP/AVM tags that allows images to "drop-in" to the WWT. The same release offers a beta-version of web-based interactive visualization of images; this includes wrappers for visualization in Python. Subsequent releases will support HEALPix (now standard in cosmic background experiments); incorporation of Montage into package managers (which enable automated management of software builds), and support for a library that will enable Montage to be called directly from Python. This next generation toolkit will inherit the architectural benefits of the current engine - component based tools, ANSI-C portability across Unix platforms and scalability for distributed processing. With the expanded functionality under development, Montage can be viewed not simply as a mosaic engine, but as a scalable, portable toolkit for managing, organizing and processing images

Interactive Visualization of the Largest Radioastronomy Cubes

3D visualization is an important data analysis and knowledge discovery tool, however, interactive visualization of large 3D astronomical datasets poses a challenge for many existing data visualization packages. We present a solution to interactively visualize larger-than-memory 3D astronomical data cubes by utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the data volume into smaller sub-volumes that are distributed over the rendering workstations. A GPU-based ray casting volume rendering is performed to generate images for each sub-volume, which are composited to generate the whole volume output, and returned to the user. Datasets including the HI Parkes All Sky Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26 GB) data cubes were used to demonstrate our framework's performance. The framework can render the GASS data cube with a maximum render time < 0.3 second with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8 GPUs. Our framework will scale to visualize larger datasets, even of Terabyte order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou