Fernando Silva-Coira

The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in‐memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called ‐raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. ‐raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in‐memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on ‐raster without a decompression procedure.

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, and compressed data formats (e.g. LAZ) have been proposed over the years. These formats are capable of transparently decompressing portions of the data, but they are not focused on solving general queries over the data. In contrast to that traditional approach, a new recent research line focuses on designing data structures that combine compression and indexation, allowing directly querying the compressed data. Compression is used to fit the data structure in main memory all the time, thus getting rid of disk accesses, and indexation is used to query the compressed data as fast as querying the uncompressed data. In this paper, we present the first data structure capable of losslessly compressing point clouds that have attributes and jointly indexing all three dimensions of space and attribute values. Our method is able to run range queries and attribute queries up to 100 times faster than previous methods.

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 present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.

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 present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.

LiDAR devices obtain a 3D representation of a space. Due to the large size of the resulting datasets, there already exist storage methods that use compression and present some properties that resemble those of compact data structures. Specifically, LAZ format allows accesses to a given datum or portion of the data without having to decompress the whole dataset and provides indexation of the stored data. However, LAZ format still have some drawbacks that should be faced. In this work, we propose a new compact data structure for the representation of a cloud of LiDAR points that supports efficient queries, providing indexing capabilities that are superior to those of LAZ format.

Summary: This paper presents 2 main contributions. The first is a compact representation of huge sets of functional data or trajectories of continuous‐time stochastic processes, which allows keeping the data always compressed even during the processing in main memory. It is oriented to facilitate the efficient computation of the sample autocovariance function without a previous decompression of the data set, by using only partial local decoding. The second contribution is a new memory‐efficient algorithm to compute the sample autocovariance function. The combination of the compact representation and the new memory‐efficient algorithm obtained in our experiments the following benefits. The compressed data occupy in the disk 75% of the space needed by the original data. The computation of the autocovariance function used up to 13 times less main memory, and run 65% faster than the classical method implemented, for example, in the R package.