Integrated Visualization with Controllable Deep Linking for Distributed Datasets
Xinxiao Li, Akira Kuroda · 2019
With visual analysis of a large IoT system where structured and unstructured datasets are collected from various distributed data sources or their edge processing units, it is analytically meaningful to have an integrated visualization composed of multiple coordinated charts and to deduce insight from datasets with visual analysis. But such a unified ensemble is difficult due to being short of explicitly available relationships among these distributed datasets. In this paper, we present an integrated visualization framework with deep linking on based of analytical relationship among distributed datasets. Considering the try-and-error aspect of visual analysis, we furtherly leverage the integrated visualization with comprehensible user interface, and implement brushing and linking individually or collaboratively.