Now showing items 1-6 of 6
GraCT: A Grammar Based Compressed Representation of Trajectories
[Abstract] We present a compressed data structure to store free trajectories of moving objects (ships over the sea, for example) allowing spatio-temporal queries. Our method, GraCT, uses a k2k2 -tree to store the absolute ...
Lossless Compression of Industrial Time Series With Direct Access
[Abstract] The new opportunities generated by the data-driven economy in the manufacturing industry have causedmany companies opt for it. However, the size of time series data that need to be captured creates theproblem ...
A New Method to Index and Store Spatio-Temporal Data
[Abstract] We propose a data structure that stores, in a compressed way, object trajectories, which at the same time, allow to efficiently response queries without the need to decompress the data. We use a data structure, ...
Space-Efficient Representations of Raster Time Series
[Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast ...
Efficient Processing of Raster and Vector Data
(Public Library of Science, 2020-01-10)
[Abstract] In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we ...
Compact and indexed representation for LiDAR point clouds
(Taylor & Francis, 2022)
[Abstract]: LiDAR devices are capable of acquiring clouds of 3D points reflecting any object around them, and adding additional attributes to each point such as color, position, time, etc. LiDAR datasets are usually large, ...