Similarity-based visual exploration of very large georeferenced multidimensional datasets

Peralta-Aranibar, Roger; Pahins, Cícero A. L.; Comba, João Luiz Dihl; Gomez-Nieto, Erick

doi:10.1145/3297280.3297556

Cited by 1 publication

(1 citation statement)

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“…By applying the cloud tool to measure the percentage of data reading and visualisation, it was possible to import 80% of the data with 431 rows for the data visualisation process, thanks to the SPICE analysis engine, which, by cleaning some null data, saved the memory consumption, which is a maximum of 1 GB in the free layer, and helped to avoid manual verification that would take a long time to find the null data. These results are congruent with those of Peralta, 2019, who achieved the visual representation of 210 million data points through heat maps (graphical representation of data) using different evaluated queries that have great importance when interpreting data and that are of vital importance in data visualisation [31,32]. This is aligned with the use of tools in the cloud, where large amounts of data can be processed to bring them to visualisation, as well as the use of heat maps.…”

Section: Discussionsupporting

confidence: 87%

Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project

Luyo,

Pacheco,

Cardenas

et al. 2023

Processes

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Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard.

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Section: Discussionsupporting

confidence: 87%