A Data Discovery and Visualization Tool for Visual Analytics of Time Series in Digital Agriculture
Jasmin K. Dhaliwal, Megan E. Galbraith, Carson Kai-Sang Leung, Da Tan · 2023
In the current era of big data, huge volumes of data can be easily generated and collected at a high velocity from a wide variety of rich data sources. Embedded in these big data-which may also contain many labels or tags-are implicit, previously unknown and potential useful information that can be discovered. Discovered knowledge helps user get a better understanding of the data. However, amounts of discovered knowledge from these huge volumes of big data can also be large. To help users comprehend the discovered knowledge, visualization approaches are in demand. In this paper, we present a data discovery and visualization tool. The tool enables users to visually monitor and explore multi-sourced, multi-tagged time-series data. It also enables users to conduct visual analytics to discover interesting data/knowledge and to visualize this information. Although we demonstrate the practicality of our tool for multi-sourced, multi-tagged time-series data from the agricultural sector, our tool can be applicable to a wide variety of other domains.