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nD-PointCloud Data Management: Continuous Levels, Adaptive Histograms, and Diverse Query Geometries - Couverture souple
Synopsis
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.
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À propos de la quatrième de couverture
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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nD-PointCloud Data Management: Continuous Levels, Adaptive Histograms, and Diverse Query Geometries (A+BE Architecture and the Built Environment)
Edité par
TU Delft, 2022
ISBN 10 : 9463665722
ISBN 13 : 9789463665728
Ancien ou d'occasion
Couverture souple
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Gut. Zustand: Gut | Seiten: 204 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 41726535/3
Quantité disponible : 3 disponible(s)
Image d'archives
nD-PointCloud Data Management: Continuous Levels, Adaptive Histograms, and Diverse Query Geometries (A+BE Architecture and the Built Environment)
Edité par
TU Delft, 2022
ISBN 10 : 9463665722
ISBN 13 : 9789463665728
Ancien ou d'occasion
Couverture souple
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Hervorragend. Zustand: Hervorragend | Seiten: 204 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 41726535/1
Quantité disponible : 6 disponible(s)