The wavelet coefficients associated with each node of the graph encode information about the signal under analysis considering all nodes in its neighborhood. However, understanding and extracting insight out of this wealth of information can be a challenging task. In this chapter, we will briefly review how the wavelet coefficients can be interpreted and explore which visual analytics resources can be leveraged. The visual representation of wavelet coefficients is still an application-dependent open problem in visualization, but recent developments introduced alternatives to specific cases, such as geo-referenced urban data and dynamic graphs.

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Wavelet-Based Visual Data Exploration

  • Alcebiades Dal Col,
  • Paola Valdivia,
  • Fabiano Petronetto,
  • Fabio Dias,
  • Claudio T. Silva,
  • L. Gustavo Nonato

摘要

The wavelet coefficients associated with each node of the graph encode information about the signal under analysis considering all nodes in its neighborhood. However, understanding and extracting insight out of this wealth of information can be a challenging task. In this chapter, we will briefly review how the wavelet coefficients can be interpreted and explore which visual analytics resources can be leveraged. The visual representation of wavelet coefficients is still an application-dependent open problem in visualization, but recent developments introduced alternatives to specific cases, such as geo-referenced urban data and dynamic graphs.