Interactive Optimization, Distance Computation and Data Estimation in Parallel Coordinates

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: February 2009
• Diploma Examination: 25. February 2009
• First Supervisor:
  • Eduard Gröller
  • Harald Piringer

Abstract

The eld of information visualization tries to nd graphical representations of data to explore regions of interest in potentially large data sets. Additionally, the use of algorithms to obtain exact solutions, which cannot be provided by basic visualization techniques, is a common approach in data analysis. This work focuses on optimization, distance computation and data estimation algorithms in the context of information visualization. Furthermore, information visualization is closely connected to interaction. To involve human abilities in the computation process, the goal is to embed these algorithms into an interactive environment. In an analysis dialog, the user observes the current solution, interprets the results and then formulates a strategy of how to proceed. This forms a tight loop of interaction, which uses human evaluation to improve the quality of the results. Optimization is a crucial approach in decision making. This work presents an interactive optimization approach, exemplied by parallel coordinates, which are a common visualization technique when dealing with multi-dimensional problems. According to this goal-based approach, multi-dimensional distance computation is discussed as well as a data estimation approach with the objective of approximating simulations by the analysis of existing values. All these approaches are integrated in an existing visual analysis framework and deal with multi-dimensional goals, which can be dened and modied interactively by the user. The goal of this work is to support decision makers to extract useful information from large data sets.

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BibTeX

@mastersthesis{froschauer-2009-iod,
  title =      "Interactive Optimization, Distance Computation and Data
               Estimation in Parallel Coordinates",
  author =     "Matthias Froschauer",
  year =       "2009",
  abstract =   "The eld of information visualization tries to nd graphical
               representations of data to explore regions of interest in
               potentially large data sets. Additionally, the use of
               algorithms to obtain exact solutions, which cannot be
               provided by basic visualization techniques, is a common
               approach in data analysis. This work focuses on
               optimization, distance computation and data estimation
               algorithms in the context of information visualization.
               Furthermore, information visualization is closely connected
               to interaction. To involve human abilities in the
               computation process, the goal is to embed these algorithms
               into an interactive environment. In an analysis dialog, the
               user observes the current solution, interprets the results
               and then formulates a strategy of how to proceed. This forms
               a tight loop of interaction, which uses human evaluation to
               improve the quality of the results. Optimization is a
               crucial approach in decision making. This work presents an
               interactive optimization approach, exemplied by parallel
               coordinates, which are a common visualization technique when
               dealing with multi-dimensional problems. According to this
               goal-based approach, multi-dimensional distance computation
               is discussed as well as a data estimation approach with the
               objective of approximating simulations by the analysis of
               existing values. All these approaches are integrated in an
               existing visual analysis framework and deal with
               multi-dimensional goals, which can be dened and modied
               interactively by the user. The goal of this work is to
               support decision makers to extract useful information from
               large data sets.",
  month =      feb,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2009/froschauer-2009-iod/",
}