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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 requirements.

Spatial models are often based on polygons both in 2D and 3D. Many Geo-ICT products support spatial data types, such as the polygon, based on the OpenGIS 'Simple Features Specification'. OpenGIS and ISO have agreed to harmonize their specifications and standards. In this paper we discuss the relevant aspects related to polygons in these standards and compare several implementations. A quite exhaustive set of test polygons (with holes) has been developed. The test results reveal significant differences in the implementations, which causes interoperability problems. Part of these differences can be explained by different interpretations (definitions) of the OpenGIS and ISO standards (do not have an equal polygon definition). Another part of these differences is due to typical implementation issues, such as alternative methods for handling tolerances. Based on these experiences we propose an unambiguous definition for polygons, which makes polygons again the stable foundation it is supposed to be in spatial modelling and analysis. Valid polygons are well defined, but as they may still cause problems during data transfer, also the concept of (valid) clean polygons is defined.

S.Psomadaki@student.tudelft.nl, (P.J.M.vanOosterom, T.P.M.Tijssen)@tudelft.nl b Deltares, 2600 MH, Delft, the Netherlands -Fedor.Baart@deltares.nl KEY WORDS: Point cloud data, Space filling curve, Spatio-temporal data, Benchmark, DBMS ABSTRACT:Point cloud usage has increased over the years. The development of low-cost sensors makes it now possible to acquire frequent point cloud measurements on a short time period (day, hour, second). Based on the requirements coming from the coastal monitoring domain, we have developed, implemented and benchmarked a spatio-temporal point cloud data management solution. For this reason, we make use of the flat model approach (one point per row) in an Index Organised Table within a RDBMS and an improved spatio-temporal organisation using a Space Filling Curve approach. Two variants coming from two extremes of the space -time continuum are also taken into account, along with two treatments of the z dimension: as attribute or as part of the space filling curve. Through executing a benchmark we elaborate on the performance -loading and querying time-, and storage required by those different approaches. Finally, we validate the correctness and suitability of our method, through an out-of-the-box way of managing dynamic point clouds.

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