nD-PointCloud Data Management

A+BE: Architecture and the Built Environment

article

Haicheng Liu¹ 

[1] Delft University of Technology

关键词: nD point clouds;    space filling curves;    continuous level of importance;    histograms;    skewed distributions;    point set coverage;    convex polytope query;    flood risk mapping;    AHN;    benchmarks;   

DOI  :  10.7480/abe.2022.12.6536

学科分类：土木及结构工程学

来源: Delft University of Technology * Faculty of Architecture

【 摘 要 】

In the Geomatics domain, a point cloud refers to a data set that records the coordinates and other attributes of a huge number of points. Conceptually, each of the attributes can be regarded as a dimension to represent a specific type of information, such as time and Level of Importance (LoI). Drastically increasing collection of high dimensional point clouds raises essential demand for smart and highly efficient data management solutions. However, effective tools are missing. File-based solutions require substantial development of data structures and algorithms. Also, with such solutions, enormous effort has to be made to integrate different data types, formats and libraries. By contrast, state-of-the-art DataBase Management Systems (DBMSs) avoid these issues, because they are initially devised for generic use of data. However, DBMSs still present limitations on efficiently indexing non-uniformly distributed points, supporting continuous LoI, and operating high dimensional data. These problems motivate the PhD research which focuses on developing a new DBMS solution. It is aimed at efficiently managing and querying massive nD point clouds to support different types of applications.

【 授权许可】

CC BY