Interactive Visual Analysis of Multi-Parameter Scientific Data

Information

• Publication Type: Habilitation Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: May 2015
• DOI: 10.34726/hss.2016.40602
• Date (Start): 2005
• Date (End): 2014

Abstract

Increasing complexity and a large number of control parameters make the design and understanding of modern engineering systems impossible without simulation. Advances in simulation technology and the ability to run multiple simulations with different sets of parameters pose new challenges for analysis techniques. The resulting data is often heterogeneous. A single data point does not contain scalars or vectors only, as usual. Instead, a single data point contains scalars, time series, and other types of mappings. Such a data model is common in many domains. Interactive visual analysis utilizes a tight feedback loop of computation/visualization and user interaction to facilitate knowledge discovery in complex datasets.

Our research extends the visual analysis technology to challenging heterogeneous data, in particular to a combination of multivariate data and more complex data types, such as functions, for example. Furthermore, we focus on developing a structured model for interactive visual analysis which supports a synergetic combination of user interaction and computational analysis.

The concept of height surfaces and function graphs is a proven and well developed mechanism for the analysis of a single mapping. The state of the art when a set of such mappings is analyzed suggested a use of different descriptors or aggregates in the analysis. Our research makes it possible to analyze a whole set of mappings (function graphs, or height surfaces, for example) while keeping the original data. We advance the interactive visual analysis to cope with complex scientific data. Most of the analysis techniques consider the data as a static source. Such an approach often hinders the analysis. We introduce a concept of interactive visual steering for simulation ensembles. We link the data generation and data exploration and analysis tasks in a single workflow. This makes it possible to tune and optimize complex systems having high dimensional parameter space and complex outputs.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2016.40602

BibTeX

@habilthesis{Matkovic_Kresimir_2015_,
  title =      "Interactive Visual Analysis of Multi-Parameter Scientific
               Data",
  author =     "Kresimir Matkovic",
  year =       "2015",
  abstract =   "Increasing complexity and a large number of control
               parameters make the design and understanding of modern
               engineering systems impossible without simulation.  Advances
               in simulation technology and the ability to run multiple
               simulations with different sets of parameters pose new
               challenges for analysis techniques. The resulting data is
               often heterogeneous. A single data point does not contain
               scalars or vectors only, as usual. Instead, a single data
               point contains scalars, time series, and other types of
               mappings. Such a data model is common in many domains.
               Interactive visual analysis utilizes a tight feedback loop
               of computation/visualization and user interaction to
               facilitate knowledge discovery in complex datasets.  Our
               research extends the visual analysis technology to
               challenging heterogeneous data, in particular to a
               combination of multivariate data and more complex data
               types, such as functions, for example. Furthermore, we focus
               on developing a structured model for interactive visual
               analysis which supports a synergetic combination of user
               interaction and computational analysis.  The concept of
               height surfaces and function graphs is a proven and well
               developed mechanism for the analysis of a single mapping.
               The state of the art when a set of such mappings is analyzed
               suggested a use of different descriptors or aggregates in
               the analysis. Our research makes it possible to analyze a
               whole set of mappings (function graphs, or height surfaces,
               for example) while keeping the original data.  We advance
               the interactive visual analysis to cope with complex
               scientific data.  Most of the analysis techniques consider
               the data as a static source. Such an approach often hinders
               the analysis. We introduce a concept of interactive visual
               steering for simulation ensembles. We link the data
               generation and data exploration and analysis tasks in a
               single workflow. This makes it possible to tune and optimize
               complex systems having high dimensional parameter space and
               complex outputs.",
  month =      may,
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2015/Matkovic_Kresimir_2015_/",
}