Abstract
One of the characteristics of big data is its internal complexity and also variety manifested in many types of datasets that are to be managed, searched, or analyzed. In their natural forms, some of the data entities are unstructured, such as texts or multimedia objects, while some are structured but too complex. In this paper, we have investigated how visualizations of various complex datasets perform in the role of universal data representations for both human users and deep learning models. In a user study, we have evaluated several visualizations of complex relational data, where some proved their superior performance with respect to the precision and speed of classification by human users. Moreover, the same visualizations also led to effective classification performance when used with deep learning models.
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Notes
- 1.
In order to ensure comparability of attributes, their values were transformed w.r.t. empirical cumulative distribution function.
- 2.
The study is accessible from http://hmon.ms.mff.cuni.cz:5002/visualrepresentation/join?guid=JvmOL0_d8aldQyy7wRTzPLJeStoHv5Cc.
- 3.
In all cases, we have used Euclidean distance. Each feature was converted to numeric value and normalized linearly between 0 and 1.
- 4.
Note that in the case of Nutrients dataset, we also selected a fixed sub-sample of displayed classes: a correct one plus three additional.
- 5.
Overall, the typical time to complete the whole study was 15–25 min.
- 6.
Note that because four classes were shown to each user in the case of Nutrients dataset, the accuracy of the random guessing would be 0.25.
- 7.
Pearson’s correlation of −0.57 and −0.94 if the results of Tabular baseline are removed.
- 8.
Note that we only evaluated visualizations that were readily available in PNG format, so we discarded the parallel coordinates and raw tabular data.
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Acknowledgements
This paper has been supported by Czech Science Foundation (GAČR) project 22-21696S and by Charles University grant SVV-260698/2023.
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Peska, L., Sixtova, I., Hoksza, D., Bernhauer, D., Skopal, T. (2023). Visual Representations for Data Analytics: User Study. In: da Silva, H.P., Cipresso, P. (eds) Computer-Human Interaction Research and Applications. CHIRA 2023. Communications in Computer and Information Science, vol 1997. Springer, Cham. https://doi.org/10.1007/978-3-031-49368-3_14
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