Towards 10^15-level point clouds management - a nD PointCloud structure

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    Drastically increasing production of point clouds as well as modern application fields like robotics and virtual reality raises essential demand for smart and highly efficient data management. Effective tools for the managing and direct use of large point clouds are missing. Current state-of-the-art database management systems (DBMS) present critical problems such as inefficient loading/indexing, lack of support of continuous Level of Detail (cLoD) and limited functionalities. Previous research has suggested and demonstrated the importance of converting property dimensions such as time and classification to organizing dimensions for efficient data management at the storage level. However, a thorough validation and theory are still missing. Besides, how new computational platforms such as the cloud technology may support data management also needs further exploration. These problems motivate the PhD research with the focus on a new data structure (nD PointCloud) which is dedicated for smartly and flexibly organizing information of large point clouds for different use cases.
    Original languageEnglish
    Title of host publicationProceedings of the 21th AGILE International Conference on Geographic Information Science
    Subtitle of host publicationGeospatial Technologies for All
    EditorsA. Mansourian, P. Pilesjö, L. Harrie, R. van Lammeren
    PublisherAssociation of Geographic Information Laboratories for Europe (AGILE)
    Number of pages7
    ISBN (Print)978-3-319-78208-9
    Publication statusPublished - 2018
    EventAGILE 2018: 21st AGILE Conference on Geographic Information Science - Lund, Sweden
    Duration: 12 Jun 201815 Jun 2018


    ConferenceAGILE 2018: 21st AGILE Conference on Geographic Information Science


    • point cloud
    • data management
    • data structure
    • database
    • dimension


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