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A learning-based approach for efficient visualization construction
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DOI：10.1016/j.visinf.2022.01.001.png)
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Abstract
En 中文
We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user's interactions. Instead, LVI directly predicts aggregates of interest for the user's data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales. (C) 2022 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd.
Keywords:
Learned index
Neural network
Visualization index
Interactive exploration
Spatiotemporal visualization
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