Towards Adaptive Trajectory Data Management: Modelling, Accessing, Distributing, and Query Optimization in Distributed Database

With the proliferation of the IoT infrastructure, the trajectory data is dynamically emerging. This data originates from a variety of moving objects, containing big volumes of multi-dimensional information such as space, time, semantics etc. The underlying information can be potentially applied to create added value through scientific research,...

SplitSFC: A database solution for massive point cloud data management

Point cloud data are gaining more importance in the Geomatics domain, with the development of modern sensing technologies like LiDAR and photogrammetry. The size of massive point clouds are growing, and the performance of traditional database solutions for its data management become insufficient. It remains a huge challenge for researchers to...

High-Performance and Scalable Agent-Based Simulation with BioDynaMo

Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high...

nD-PointCloud Data Management: continuous levels, adaptive histograms, and diverse query geometries

In the Geomatics domain, a point cloud refers to a data set which records the coordinates and other attributes of a huge number of points. Conceptually, each of these attributes can be regarded as a dimension, representing a specific type of information. Apart from routinely concerned spatio-temporal dimensions for coordinates, other dimensions...

PCSERVE-ND-Pointclouds Retrieval over the Web

We investigate how PCServe, a web service for disseminating massive point clouds, performs for read-only access (i.e. a visualization application). PCServe is backed by a database model based on Space Filling Curves. By adding a virtual hierarchy of blocks to the database, we can support different visualization applications for retrieval of...

Organizing and visualizing point clouds with continuous levels of detail

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower...

An efficient nd-point data structure for querying flood risk

Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original...

manage 4D historical AIS data by Space Filling Curve

This MSc thesis aims to research how to efficiently manage 4D AIS data (Longitude, Latitude, Time, and MMSI (the ID of the vessel)) to do the fast query by using the Space Filling curve in PostgreSQL. The AIS is the Automatic Identification System which is born because the frequent occurrence of maritime accidents has caused casualties and...

HistSFC: Optimization for nD massive spatial points querying

Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B+- tree could be utilized. Based on the basic structure, this paper develops a generic HistSFC approach which utilizes a histogram tree recording...

Space Filling Curves for MRI Sampling

A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed...

A parallel N-dimensional Space-Filling Curve library and its application in massive point cloud management

Because of their locality preservation properties, Space-Filling Curves (SFC) have been widely used in massive point dataset management. However, the completeness, universality, and scalability of current SFC implementations are still not well resolved. To address this problem, a generic n-dimensional (nD) SFC library is proposed and...

Clustering and indexing historic vessel movement data with space filling curves

This paper reports on the result of an on-going study using Space Filling Curves (SFCs) for indexing and clustering vessel movement message data (obtained via the Automated Identification System, AIS) inside a geographical Database Management System (Geo-DBMS). With AIS, vessels transmit their positions in intervals ranging from 2 seconds to...

Using a Space Filling Curve for the Management of Dynamic Point Cloud Data in a Relational DBMS

The rapid developments in the field of point cloud acquisition technologies have allowed point clouds to become an important source of information for many applications. One of the newest applications of point clouds concerns the monitoring of the coast. Many countries, among which the Netherlands, use this source of data in order to determine...

Using a space filling curve approach for the management of dynamic point clouds

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...

Point Clouds in a Database: Data Management within an Engineering Company

With laser scanning (including laser altimetry and multi-beam echo sounding), many data points, called point clouds, are measured. The interest in point clouds is increasing. Depending on the laser scanner, the measurement set-up and the technique, the number of points in one dataset can vary from a few hundred points to over a billion points....