arXiv subjects

Guillermo de Bernardo

Publications and source records attributed to Guillermo de Bernardo.

13 recordsLinked to original sources

arXiv2022

Space/time-efficient RDF stores based on circular suffix sorting

In recent years, RDF has gained popularity as a format for the standardized publication and exchange of information in the Web of Data. In this paper we introduce RDFCSA, a data structure that is able to self-index an RDF dataset in small space and supports efficient querying. RDFCSA regards the triples of the RDF store as short circular strings and applies suffix sorting on those strings, so that triple-pattern queries reduce to prefix searching on the string set. The RDF store is then represented compactly using a Compressed Suffix Array (CSA), a proved technology in text indexing that efficiently supports prefix searches. Our experiments show that RDFCSA provides a compact RDF representation, using less than 60% of the space required by the raw data, and yields fast and consistent query times when answering triple-pattern queries (a few microseconds per result). We also support join queries, a key component of most SPARQL queries. RDFCSA is shown to provide an excellent space/time tradeoff, typically using much less space than alternatives that compete in time.

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arXiv2021

Faster Compressed Quadtrees

Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using $\Oh{1}$ bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

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arXiv2020

Revisiting compact RDF stores based on k2-trees

We present a new compact representation to efficiently store and query large RDF datasets in main memory. Our proposal, called BMatrix, is based on the k2-tree, a data structure devised to represent binary matrices in a compressed way, and aims at improving the results of previous state-of-the-art alternatives, especially in datasets with a relatively large number of predicates. We introduce our technique, together with some improvements on the basic k2-tree that can be applied to our solution in order to boost compression. Experimental results in the flagship RDF dataset DBPedia show that our proposal achieves better compression than existing alternatives, while yielding competitive query times, particularly in the most frequent triple patterns and in queries with unbound predicate, in which we outperform existing solutions.

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arXiv2019

On dynamic succinct graph representations

We address the problem of representing dynamic graphs using $k^2$-trees. The $k^2$-tree data structure is one of the succinct data structures proposed for representing static graphs, and binary relations in general. It relies on compact representations of bit vectors. Hence, by relying on compact representations of dynamic bit vectors, we can also represent dynamic graphs. In this paper we follow instead the ideas by Munro {\em et al.}, and we present an alternative implementation for representing dynamic graphs using $k^2$-trees. Our experimental results show that this new implementation is competitive in practice.

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arXiv2019

Faster Dynamic Compressed d-ary Relations

The $k^2$-tree is a successful compact representation of binary relations that exhibit sparseness and/or clustering properties. It can be extended to $d$ dimensions, where it is called a $k^d$-tree. The representation boils down to a long bitvector. We show that interpreting the $k^d$-tree as a dynamic trie on the Morton codes of the points, instead of as a dynamic representation of the bitvector as done in previous work, yields operation times that are below the lower bound of dynamic bitvectors and offers improved time performance in practice.

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arXiv2019

Extending General Compact Querieable Representations to GIS Applications

The raster model is commonly used for the representation of images in many domains, and is especially useful in Geographic Information Systems (GIS) to store information about continuous variables of the space (elevation, temperature, etc.). Current representations of raster data are usually designed for external memory or, when stored in main memory, lack efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We present different representations for binary raster data, general raster data and time-evolving raster data. We experimentally compare our proposals with traditional storage mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show that our structures are up to 10 times smaller than classical linear quadtrees, and even comparable in space to non-querieable representations of raster data, while efficiently answering a number of typical queries.

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arXiv2019

Improved Compressed String Dictionaries

We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory. Our main technique is a combination of hierarchical Front-coding with ideas from longest-common-prefix computation in suffix arrays. Our data structures yield relevant space-time tradeoffs in real-world dictionaries. We focus on two domains where string dictionaries are extensively used and efficient compression is required: URL collections, a key element in Web graphs and applications such as Web mining; and collections of URIs and literals, the basic components of RDF datasets. Our experiments show that our data structures achieve better compression than the state-of-the-art alternatives while providing very competitive query times.

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arXiv2018

Towards a compact representation of temporal rasters

Big research efforts have been devoted to efficiently manage spatio-temporal data. However, most works focused on vectorial data, and much less, on raster data. This work presents a new representation for raster data that evolve along time named Temporal k^2 raster. It faces the two main issues that arise when dealing with spatio-temporal data: the space consumption and the query response times. It extends a compact data structure for raster data in order to manage time and thus, it is possible to query it directly in compressed form, instead of the classical approach that requires a complete decompression before any manipulation. In addition, in the same compressed space, the new data structure includes two indexes: a spatial index and an index on the values of the cells, thus becoming a self-index for raster data.

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arXiv2018

About BIRDS project (Bioinformatics and Information Retrieval Data Structures Analysis and Design)

BIRDS stands for "Bioinformatics and Information Retrieval Data Structures analysis and design" and is a 4-year project (2016--2019) that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 690941. The overall goal of BIRDS is to establish a long term international network involving leading researchers in the development of efficient data structures in the fields of Bioinformatics and Information Retrieval, to strengthen the partnership through the exchange of knowledge and expertise, and to develop integrated approaches to improve current approaches in both fields. The research will address challenges in storing, processing, indexing, searching and navigating genome-scale data by designing new algorithms and data structures for sequence analysis, networks representation or compressing and indexing repetitive data. BIRDS project is carried out by 7 research institutions from Australia (University of Melbourne), Chile (University of Chile and University of Concepción), Finland (University of Helsinki), Japan (Kyushu University), Portugal (Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, INESC-ID), and Spain (University of A Coruña), and a Spanish SME (Enxenio S.L.). It is coordinated by the University of A Coruña (Spain).

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arXiv2018

Compact Representations of Event Sequences

We introduce a new technique for the efficient management of large sequences of multidimensional data, which takes advantage of regularities that arise in real-world datasets and supports different types of aggregation queries. More importantly, our representation is flexible in the sense that the relevant dimensions and queries may be used to guide the construction process, easily providing a space-time tradeoff depending on the relevant queries in the domain. We provide two alternative representations for sequences of multidimensional data and describe the techniques to efficiently store the datasets and to perform aggregation queries over the compressed representation. We perform experimental evaluation on realistic datasets, showing the space efficiency and query capabilities of our proposal.

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arXiv2017

Compressed Representation of Dynamic Binary Relations with Applications

We introduce a dynamic data structure for the compact representation of binary relations $\mathcal{R} \subseteq A \times B$. The data structure is a dynamic variant of the k$^2$-tree, a static compact representation that takes advantage of clustering in the binary relation to achieve compression. Our structure can efficiently check whether two objects $(a,b) \in A \times B$ are related, and list the objects of $B$ related to some $a \in A$ and vice versa. Additionally, our structure allows inserting and deleting pairs $(a,b)$ in the relation, as well as modifying the base sets $A$ and $B$. We test our dynamic data structure in different contexts, including the representation of Web graphs and RDF databases. Our experiments show that our dynamic data structure achieves good compression ratios and fast query times, close to those of a static representation, while also providing efficient support for updates in the represented binary relation.

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arXiv2017

A succinct data structure for self-indexing ternary relations

The representation of binary relations has been intensively studied and many different theoretical and practical representations have been proposed to answer the usual queries in multiple domains. However, ternary relations have not received as much attention, even though many real-world applications require the processing of ternary relations. In this paper we present a new compressed and self-indexed data structure that we call Interleaved $K^2$-tree (I$K^2$-tree), designed to compactly represent and efficiently query general ternary relations. The I$K^2$-tree is an evolution of an existing data structure, the $K^2$-tree, initially designed to represent Web graphs and later applied to other domains. The I$K^2$-tree is able to extend the $K^2$-tree to represent a ternary relation, based on the idea of decomposing it into a collection of binary relations but providing indexing capabilities in all the three dimensions. We present different ways to use I$K^2$-tree to model different types of ternary relations using as reference two typical domains: RDF and Temporal Graphs. We also experimentally evaluate our representations comparing them in space usage and performance with other solutions of the state of the art.

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arXiv2016

A new method to index and store spatio-temporal data

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, called $k^{2}$-tree, to store the full position of all objects at regular time intervals. For storing the positions of objects between two time instants represented with $k^{2}$-trees, we only encode the relative movements. In order to save space, those relative moments are encoded with only one integer, instead of two (x,y)-coordinates. Moreover, the resulting integers are further compressed with a technique that allows us to manipulate those movements directly in compressed form. In this paper, we show an experimental evaluation of this structure, which shows important savings in space and good response times.

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