Pedro Campos Vieira

Pedro Campos Vieira

I am Pedro, a researcher in Artificial Intelligence and Data Science with a Master's degree in the field. My broad area of interest is Network Science and Machine Learning. Currently I focus on topics at the intersection of geometry, topology, and deep learning. I'm aiming to continue this path through a Ph.D. in Topological and Geometric Deep Learning.

The Dynamic-Loose octree : a spatial index structure towards time-efficiency

Published in Journal peer-reviewed to be published in mid-to-late Winter 2025., 2024

Abstract

Octrees are popular static structures for spatial data indexing to which, over the years, several enhancements were proposed. One of these enhancements provided the capability of dealing with dynamic scenarios. Another, the Loose octree enhancement, focused on improving the structure efficiency. These two enhancements, when put together generate performance-breaking problems. In this work, we study the merge of the two enhancements, while we identify and solve the performance-breaking problems that arise from their coupling. The result is the Dynamic-Loose octree: an index structure, loosely enhanced, capable of dynamically adjusting according to the insertion and removal of data. To validate the proposed index structure, we analyse its runtime performance by testing it in a multitude of dynamic scenarios of growing volumes of data. Results showed that this novel octree variant is a time-efficient index structure, accomplishing logarithmic performance on all management operations.

Note

  • There is no public version of the paper out yet.

Recommended citation: Carneiro, E., C. Vieira, P., Carvalho, A. V., Amaro, M., The Dynamic-Loose octree : a spatial index structure towards time-efficiency.
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