AID: An Adaptive Image Data Index for Interactive Multilevel Visualization
AID: An Adaptive Image Data Index for Interactive Multilevel Visualization
复制标题
AID：用于交互式多级可视化的自适应图像数据索引
DOI：
10.1109/icde.2019.00150
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shipra Jais
中科院分区：
文献类型：
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作者：
Saheli Ghosh;Ahmed Eldawy;Shipra Jais
可视化已经成为大数据管理和探索的一个组成部分。通过处理数据的几何形状以及其他属性，大空间数据在地图上可视化。为了加快大空间数据的可视化速度，目前有两种可视化索引，图像索引和数据索引。图像索引提供交互式可视化，但需要很长的索引时间，而数据索引构建速度很快，但对于大数据来说不是交互式的。本文介绍了第一个自适应可视化索引，它结合了数据和图像，提供了一个可扩展的，交互式的可视化，同时最大限度地减少索引的大小和索引的建设时间。他们的关键思想是识别出可视化成本高的区域，并将其存储为部分图像。其余区域作为原始数据存储，并在查询时实时可视化。初步结果表明，该索引可以提供高度互动的可视化与最小的索引时间。
Visualization has become an integral part of big data management and exploration. Big spatial data is visualized on a map by processing the geometry of the data as well as other attributes. To speed up big spatial data visualization, two visualization indexes are currently available, image indexes and data indexes. Image indexes provide an interactive visualization but require a long indexing time, while data indexes are fast to build but are not interactive for big data. This paper introduces the first adaptive visualization index that combines both data and images to provide a scalable, interactive visualization while minimizing the index size and index construction time. They key idea is to identify the regions that are costly to visualize and store them as partial images. The remaining regions are stored as raw data and are visualized on-the-fly at query time. The preliminary results show that the proposed index can provide highly interactive visualization with a minimal indexing time.