Sandra Álvarez-García

Compact Querieable Representations of Raster Data

In Geographic Information Systems (GIS) the attributes of the space (altitude, temperature, etc.) are usually represented using a raster model. There are no compact representations of raster data that provide efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We experimentally compare our proposals with traditional storage mechanisms for raster data, showing that our structures obtain competitive space performance while efficiently answering range queries involving the values stored in the raster.

Compressed vertical partitioning for efficient RDF management

The Web of Data has been gaining momentum in recent years. This leads to increasingly publish more and more semi-structured datasets following, in many cases, the RDF (Resource Description Framework) data model based on atomic triple units of subject, predicate, and object. Although it is a very simple model, specific compression methods become necessary because datasets are increasingly larger and various scalability issues arise around their organization and storage. This requirement is even more restrictive in RDF stores because efficient SPARQL solution on the compressed RDF datasets is also required. This article introduces a novel RDF indexing technique that supports efficient SPARQL solution in compressed space. Our technique, called

GraphGen: A Tool for Automatic Generation of Multipartite Graphs from Arbitrary Data

\hbox {k}^2

Automatic multi-partite graph generation from arbitrary data

k2-triples, uses the predicate to vertically partition the dataset into disjoint subsets of pairs (subject, object), one per predicate. These subsets are represented as binary matrices of subjects

Compact and efficient representation of general graph databases

\times

Compressed Vertical Partitioning for Full-In-Memory RDF Management

× objects in which 1-bits mean that the corresponding triple exists in the dataset. This model results in very sparse matrices, which are efficiently compressed using