GIS navigation boosted by column stores

Earth observation sciences, astronomy, and seismology have large data sets which have inherently rich spatial and geospatial information. In combination with large collections of semantically rich objects which have a large number of thematic properties, they form a new source of knowledge for urban planning, smart cities and natural resource management. Modeling and storing these properties indicating the relationships between them is best handled in a relational database. Furthermore, the scalability requirements posed by the latest 26-attribute light detection and ranging (LIDAR) data sets are a challenge for file-based solutions. In this demo we show how to query a 640 billion point data set using a column store enriched with GIS functionality. Through a lightweight and cache conscious secondary index called Imprints, spatial queries performance on a flat table storage is comparable to traditional file-based solutions. All the results are visualised in real time using QGIS.