15. – 20. Juli 2007, Dagstuhl Seminar 07291
Auskunft zu diesem Dagstuhl Seminar erteilt
Scientific visualization (SV) is concerned with the use of computer-generated images to aid the understanding, analysis and manipulation of data. Since its beginning in the early 90's, the techniques of SV have aided scientists, engineers, medical practitioners, and others in the study of a wide variety of data sets including, for example, high performance computing simulations, measured data from scanners (CAT, MR, confocal microscopy), internet traffic, and financial records. One of the important themes being nurtured under the aegis of Scientific Visualization is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes and simulations involving voluminous data sets across diverse scientific disciplines. Since vision dominates our sensory input, strong efforts have been made to bring the mathematical abstraction and modeling to our eyes through the mediation of computer graphics. This interplay between various application areas and their specific problem solving visualization techniques was emphasized in the proposed seminar.
Reflecting the heterogeous structure of Scientific Visualization, we will focus on the following:
- Visual Analytics:
- The fields of information analysis and visualization are rapidly merging to create a new approach to extracting meaning from massive, complex, evolving data sources and stream. Visual analytics is the science of analytical reasoning facilitated by interactive, visual interfaces. The goal of visual analytics is to obtain insight into massive, dynamic and often conflicting pieces and formats of information; to detect the expected and to discover the unexpected; and to yield timely assessments with evidence and confidence levels.
- Quality Measures:
- It is vital for the visualizatioin field to establish quality metrics. An intrinsic quality metric will tremendously simplify the development and evaluation of various algorithms. The establishment of quality metrics will also advance the acceptance and use of visualization in industrial and medical applications.
- Ubiquitous Visualization:
- As ubiquitous computing is getting increased attention, also visual display of everywhere available data is necessary. Challenges include: heterogeneous output devices, novel interaction metaphors, network bandwidth (availability, reliability), graceful degradation of algorithms with respect to largely varying resources, invivo visualization (real time, no pre-processing, robust).
- Multifield and multiscale visualization:
- The output of the majority of computational science and engineering simulations is typically a combination of fields, so called multifield data, involving a number of scalar fields, vector fields, or tensor fields. Similarly, data collected experimentally is often multifield in nature (and from multiple sources). The ability to effectively visualize multiple fields simultaneously, for both computational and experimental data, can greatly enhance scientific analysis and understanding. Multiscale problems with scale differences of several orders of magnitude in CFD, nanotechnology, biomedical engineering and proteomics pose challenging problems for data analysis. The state of the art in multiscale visualization considerably lags behind that of multiscale simulation. Novel solutions to multiscale and multifield visualization problems have the potential for a large impact on scientific endeavors.
Our Dagstuhl workshop was arranged into three general types of sessions: Senior Short Talks, In-Depth Research Talks, and Break-out Sessions. The senior short talks were designed to pose research challenges and approaches for the future and had a very short presentation followed by long, lively discussions. The in-depth research talks allowed for detailed presentation of research approaches and projects, as well as a special session on education challenges/approaches within scientific visualization. The break-out sessions were used to stimulate group focused discussions on important topics and actions for the future.
Dagstuhl Seminar Series
- 18041: "Foundations of Data Visualization" (2018)
- 14231: "Scientific Visualization" (2014)
- 11231: "Scientific Visualization" (2011)
- 09251: "Scientific Visualization" (2009)
- 05231: "Scientific Visualization: Challenges for the Future" (2005)
- 03231: "Scientific Visualization: Extracting Information and Knowledge from Scientific Data Sets" (2003)
- 00211: "Scientific Visualization" (2000)
- 9724: "Scientific Visualization" (1997)
- 9421: "Scientific Visualization" (1994)
- 9135: "Scientific Visualization" (1991)
- Data Bases / Information Retrieval
- Modelling / Simulation
- Security / Cryptography
- Markov chains
- Numerical methods
- Web information retrieval
- Performance evaluation
- Intrusion detection
- Aggregation-disaggregation methods
- Graph-oriented decomposition