AID*: A Spatial Index for Visual Exploration of Geo-Spatial Data

Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. This paper introduces a new index structure, termed AID*, that facilitates the visual exploration of an arbitrarily large number of big spatial datasets on a single machine. The AID* index defines multi-resolution fixed-size tiles on the input and classifies them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. Our experiments, on up-to 1 TB of data and 27 billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity.