Abstract
While information is growing exponentially, datasets are getting bigger and bigger containing valuable information that can expand human knowledge. To extract meaningful information from these dense datasets, the need for effective graphical representations that take advantage of the human’s visual perception capabilities is revealed. The visualization of this kind of data is a complex task. These big datasets are in general inherently multidimensional (n-D), facing the challenge of finding suitable mappings from the n-D space to a 2D or 3D space. Even though multiple visualization methods have been developed for n-D data, many of them do not allow the complete restoration of the data from its reduced representation and/or do not represent the complete n-D dataset. The General Lines Coordinates (GLC) are reversible visual representations that preserve n-D information for knowledge discovery. In this paper, we present the npGLC-Vis Library, a data visualization library supporting Non-Paired General Line Coordinates (npGLC) with associated traditional interactions like brushing, zooming, and panning. npGLC-Vis is a collection of visualization methods, designed for experimenting with npGLC techniques in the development of visualization applications. We present the library design and implementation, exemplifying it through the representation of different datasets.
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Luque, L.E., Ganuza, M.L., Antonini, A.S., Castro, S.M. (2021). npGLC-Vis Library for Multidimensional Data Visualization. In: Naiouf, M., Rucci, E., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2021. Communications in Computer and Information Science, vol 1444. Springer, Cham. https://doi.org/10.1007/978-3-030-84825-5_14
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