@inbook{64a7595b1fa94ef481d8e6bc088b2ad3,
title = "Wavelet-based visual data exploration",
abstract = "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.",
author = "{Dal Col}, Alcebiades and Paola Valdivia and Fabiano Petronetto and Fabio Dias and Silva, {Claudio T.} and {Gustavo Nonato}, L.",
note = "Funding Information: Acknowledgements Grants 2011/ 22749-8, 2013/ 14089-3, 2014/ 12815-1, 2015/ 03330-7, 2016/ 04391-2, and 2016/ 04190-7 from S{\~a}o Paulo Research Foundation (FAPESP). The views expressed are those of the authors and do not reflect the official policy or position of the S{\~a}o Paulo Research Foundation. Fabio Dias was partially supported by the Urban Genome Project, an University of Toronto Connaught Global Challenge grant. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.",
year = "2019",
doi = "10.1007/978-3-030-03574-7_14",
language = "English (US)",
series = "Signals and Communication Technology",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "459--478",
booktitle = "Signals and Communication Technology",
address = "Germany",
}