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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Multi-view Approach for Visual Exploration of Temporal Multi-dimensional Vehicle Experiment Data
verfasst von : Xianglei Zhu, Haining Tong, Shuai Zhao, Quan Wen, Jie Li
Erschienen in: Advances in Intelligent Systems and Interactive Applications
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Abstract
We introduce a new visual analytics approach to the vehicle experiment data with multi-dimensional and time-series characteristics. Our approach integrates four visualization techniques to explore potential patterns hidden in huge amounts of actual experiment data. Overview view is the main view, which is used to show high level data characteristics of the experiment process and provide an intuitive and compact view for analyzing temporal and multi-dimensional patterns. Other three visualization techniques can be viewed as the complementary views, specialized in analyzing temporal features, relationships between different attributes and vehicle status variation, which are also important domain tasks. A case study has been conducted using a real-world dataset to verify the effectiveness and scalability of our approach.