Visually Mining the Datacube using a Pixel-Oriented Technique

International audienceThis paper introduces a new technique easing the navigation and interactive exploration of huge multidimensional datasets. Following the pixel-oriented paradigm, the key ingredients enabling the interactive navigation of extreme volumes of data rely on a set of functions bijectively mapping data elements to screen pixels. The use of the mapping from data elements to pixels constrain the computational complexity for the rendering process to be linear with respect to the number of rendered pixels on the screen as opposed to the dataset size. Our method furthermore allows the implementation of usual information visualization techniques such as zoom and pan, anamorphosis and texturing. As a proof-of-concept, we show how our technique can be adapted to interactively explore the Datacube, turning our approach into an efficient system for visual datamining. We report experiments conducted on a Datacube containing 50 millions of items. To our knowledge, our technique outperforms all existing ones and push the scalability limit close to the billion of elements. Supporting all basic navigation techniques, and being moreover flexible makes it easily reusable for a large number of applications

Visually Mining the Datacube using a Pixel-Oriented Technique

International audienceThis paper introduces a new technique easing the navigation and interactive exploration of huge multidimensional datasets. Following the pixel-oriented paradigm, the key ingredients enabling the interactive navigation of extreme volumes of data rely on a set of functions bijectively mapping data elements to screen pixels. The use of the mapping from data elements to pixels constrain the computational complexity for the rendering process to be linear with respect to the number of rendered pixels on the screen as opposed to the dataset size. Our method furthermore allows the implementation of usual information visualization techniques such as zoom and pan, anamorphosis and texturing. As a proof-of-concept, we show how our technique can be adapted to interactively explore the Datacube, turning our approach into an efficient system for visual datamining. We report experiments conducted on a Datacube containing 50 millions of items. To our knowledge, our technique outperforms all existing ones and push the scalability limit close to the billion of elements. Supporting all basic navigation techniques, and being moreover flexible makes it easily reusable for a large number of applications

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

JNets: Exploring networks by integrating annotation

<p>Abstract</p> <p>Background</p> <p>A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.</p> <p>Results</p> <p>JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response.</p> <p>Conclusion</p> <p>JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at <url>http://www.manchester.ac.uk/bioinformatics/jnets</url>.</p

Visual and interactive exploration of point data

Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data

SlicerAstro: a 3-D interactive visual analytics tool for HI data

SKA precursors are capable of detecting hundreds of galaxies in HI in a single 12 hours pointing. In deeper surveys one will probe more easily faint HI structures, typically located in the vicinity of galaxies, such as tails, filaments, and extraplanar gas. The importance of interactive visualization has proven to be fundamental for the exploration of such data as it helps users to receive immediate feedback when manipulating the data. We have developed SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on traditional 2-D input/output hardware. These capabilities enhance the data inspection, allowing faster analysis of complex sources than with traditional tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing. We demonstrate the capabilities of the current stable binary release of SlicerAstro, which offers the following features: i) handling of FITS files and astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii) interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D modeling. In addition, SlicerAstro has been designed with a strong, stable and modular C++ core, and its classes are also accessible via Python scripting, allowing great flexibility for user-customized visualization and analysis tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing. SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr

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

Visualization of spectral images

Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. Visualization of these huge data sets is not a straightforward issue. There are three principal ways in which spectral data can be presented; as spectra, as image and in feature space. This paper describes several visualization methods and their suitability in the different steps in the research cycle. Combinations of the three presentation methods and dynamic interaction between them, adds significant to the usability. Examples of some software implementations are given. Also the application of volume visualization methods to display spectral images is shown to be valuabl