In this paper we propose to treat point clouds as a first-class representation (similar to vector or raster representations), with the nD-PointCloud as the solution for this, offering deep integration of space, time and scale. For efficiency rea-sons spatial indexing and clustering of these large point clouds is extremely important and this is obtained based on a Space Filling Curved (SFC). In order to get beyond the current state of the art of storing/ managing point clouds in files, a DBMS solution is presented (with all benefits: integration with other data types, scalability, multi-user, transaction support, etc.). Finally, a DBMS SFC interface specification for point clouds is proposed.@en

Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure are benchmarked to determine the best solution for storing and querying large MD hydrologic datasets. Expert consultancy was conducted to establish benchmark sets, with the HydroNET-4 system being utilized to provide the benchmark environment. In the final benchmark tests, the effect of data storage configurations, elaborating chunk size, dimension order (spatio-temporal clustering) and compression on the query performance, is explored. Results indicate that for big hydrologic MD data management, the properly chunked NetCDF-4 solution without compression is, in general, more efficient than the SciDB DBMS. However, benefits of a DBMS should not be neglected, for example, the integration with other data types, smart caching strategies, transaction support, scalability, and out-of-The-box support for parallelization.@en

Lidar, photogrammetry, and various other survey technologies enable the collection of massive point clouds. Faced with hundreds of billions or trillions of points the traditional solutions for handling point clouds usually under-perform even for classical loading and retrieving operations. To obtain insight in the features affecting performance the authors carried out single-user tests with different storage models on various systems, including Oracle Spatial and Graph, PostgreSQL-PostGIS, MonetDB and LAStools (during the second half of 2014). In the summer of 2015, the tests are further extended with the latest developments of the systems, including the new version of Point Data Abstraction Library (PDAL) with efficient compression. Web services based on point cloud data are becoming popular and they have requirements that most of the available point cloud data management systems can not fulfil. This means that specific custom-made solutions are constructed. We identify the requirements of these web services and propose a realistic benchmark extension, including multi-user and level-of-detail queries. This helps in defining the future lines of work for more generic point cloud data management systems, supporting such increasingly demanded web services.@en

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.@en