Benchmarking and improving point cloud data management in MonetDB

The popularity, availability and sizes of point cloud data sets are increasing, thus raising interesting data management and processing challenges. Various software solutions are available for the management of point cloud data. A benchmark for point cloud data management systems was defined and it was executed for several solutions. In this paper we focus on the solutions based on the column-store MonetDB, the generic out-of-the-box approach is compared with two alternative approaches that exploit the spatial coherence of the data to improve the data access and to minimize the storage requirement

A spatial column-store to triangulate the Netherlands on the fly

3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS