AID: An Adaptive Image Data Index for Interactive Multilevel Visualization

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.