Adaptive multiresolution visualization of large multidimensional multivariate scientific datasets

The sizes of today\u27s scientific datasets range from megabytes to terabytes, making it impossible to directly browse the raw datasets visually. This presents significant challenges for visualization scientists who are interested in supporting these datasets. In this thesis, we present an adaptive data representation model which can be utilized with many of the commonly employed visualization techniques when dealing with large amounts of data. Our hierarchical design also alleviates the long standing visualization problem due to limited display space. The idea is based on using compactly supported orthogonal wavelets and additional downsizing techniques to generate a hierarchy of fine to coarse approximations of a very large dataset for visualization. An adaptive data hierarchy, which contains authentic multiresolution approximations and the corresponding error, has many advantages over the original data. First, it allows scientists to visualize the overall structure of a dataset by browsing its coarse approximations. Second, the fine approximations of the hierarchy provide local details of the interesting data subsets. Third, the error of the data representation can provide the scientist with information about the authenticity of the data approximation. Finally, in a client-server network environment, a coarse representation can increase the efficiency of a visualization process by quickly giving users a rough idea of the dataset before they decide whether to continue the transmission or to abort it. For datasets which require long rendering time, an authentic approximation of a very large dataset can speed up the visualization process greatly. Variations on the main wavelet-based multiresolution hierarchy described in this thesis also lead to other multiresolution representation mechanisms. For example, we investigate the uses of norm projections and principal components to build multiresolution data hierarchies of large multivariate datasets. This leads to the development of a more flexible dual multiresolution visualization environment for large data exploration. We present the results of experimental studies of our adaptive multiresolution representation using wavelets. Utilizing a multiresolution data hierarchy, we illustrate that information access from a dataset with tens of millions of data values can be achieved in real time. Based on these results, we propose procedures to assist in generating a multiresolution hierarchy of a large dataset. For example, the findings indicate that an ordinary computed tomography volume dataset can be represented effectively for some tasks by an adaptive data hierarchy with less than 1.5% of its original size

Report on the Dagstuhl Seminar on Visualization and Monitoring of Network Traffic

The Dagstuhl Seminar on Visualization and Monitoring of Network Traffic took place May 17-20, 2009 in Dagstuhl, Germany. Dagstuhl seminars promote personal interaction and open discussion of results as well as new ideas. Unlike at most conferences, the focus is not solely on the presentation of established results but also, and in equal parts, to presentation of results, ideas, sketches, and open problems. The aim of this particular seminar was to bring together experts from the information visualization community and the networking community in order to discuss the state of the art of monitoring and visualization of network traffic. People from the different research communities involved jointly organized the seminar. The co-chairs of the seminar from the networking community were Aiko Pras (University of Twente) and Jürgen Schönwälder (Jacobs University Bremen). The co-chairs from the visualization community were Daniel A. Keim (University of Konstanz) and Pak Chung Wong (Pacific Northwest National Laboratory). Florian Mansmann (University of Konstanz) helped with producing this report. The seminar was organized and supported by Schloss Dagstuhl and the European Network of Excellence for the Management of Internet Technologies and Complex Systems (EMANICS)

Extreme-scale visual analytics

pre-printThe September/October 2004 CG&A introduced the term visual analytics (VA) to the computer science literature.1 In 2005, an international advisory panel with representatives from academia, industry, and government defined VA as "the science of analytical reasoning facilitated by interactive visual interfaces."2 VA has grown rapidly into a vibrant R&D community offering data analytics and exploration solutions to both scientific and nonscientific problems in diverse domains and platforms. This special issue further examines advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications

09211 Abstracts Collection -- Visualization and Monitoring of Network Traffic

From 17.05. to 20.05.2009, the Dagstuhl Seminar 09211 ``Visualization and Monitoring of Network Traffic \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

The properties of early-type galaxies in the Ursa Major cluster

Using SDSS-DR7 and NASA/IPAC Extragalactic Database spectroscopic data, we identify 166 galaxies as members of the Ursa Major cluster with Mr < -13.5 mag. We morphological classify all galaxies by means of carefully inspecting g-, r-, i-band colour and monochromatic images. We show that the Ursa Major cluster is dominated by late-type galaxies, but also contains a significant number of early- type galaxies, particularly in the dwarf regime. We present further evidence for the existence of several subgroups in the cluster, consistent with previous findings. The early-type fraction is found to correlate with the mass of the subgroup. We also investigate environmental effects by comparing the properties of the Ursa Major early-type dwarf galaxies to those of the Virgo cluster. In contrast to the Virgo, the red sequence of the Ursa Major cluster is only sparsely populated in the optical and ultraviolet colour-magnitude relations. It also shows a statistically significant gap between -18 < Mr < -17 mag, i.e. the Ursa Major cluster lacks early-type dwarf galaxies at the bright end of their luminosity function. We discover that the majority of early-type dwarf galaxies in the Ursa Major cluster have blue cores with hints of recent or ongoing star formation. We suggest that gravitational tidal interactions can trigger central blue star forming regions in early-type dwarfs. After that, star formation would only fade completely when the galaxies experience ram pressure stripping or harassment, both of which are nearly absent in the Ursa Major cluster.Comment: 19 pages, 18 figures, 2 tables, Accepted for publication in MNRA

The Extended Virgo Cluster Catalog

We present a new catalog of galaxies in the wider region of the Virgo cluster, based on the Sloan Digital Sky Survey (SDSS) Data Release 7. The Extended Virgo Cluster Catalog (EVCC) covers an area of 725 deg^2 or 60.1 Mpc^2. It is 5.2 times larger than the footprint of the classical Virgo Cluster Catalog (VCC) and reaches out to 3.5 times the virial radius of the Virgo cluster. We selected 1324 spectroscopically targeted galaxies with radial velocities less than 3000 kms^-1. In addition, 265 galaxies that have been missed in the SDSS spectroscopic survey but have available redshifts in the NASA Extragalactic Database are also included. Our selection process secured a total of 1589 galaxies of which 676 galaxies are not included in the VCC. The certain and possible cluster members are defined by means of redshift comparison with a cluster infall model. We employed two independent and complementary galaxy classification schemes: the traditional morphological classification based on the visual inspection of optical images and a characterization of galaxies from their spectroscopic features. SDSS u, g, r, i, and z passband photometry of all EVCC galaxies was performed using Source Extractor. We compare the EVCC galaxies with the VCC in terms of morphology, spatial distribution, and luminosity function. The EVCC defines a comprehensive galaxy sample covering a wider range in galaxy density that is significantly different from the inner region of the Virgo cluster. It will be the foundation for forthcoming galaxy evolution studies in the extended Virgo cluster region, complementing ongoing and planned Virgo cluster surveys at various wavelengths.Comment: 69 pages, 29 figures, 4 tables, accepted for publication in the ApJ