García-García, Francisco

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Garcia-Garcia, Francisco

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University of Almeria, Spain
Dept. of Informatics, University of Almeria, Spain
Universidad de Almeria, Spain

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  • Artículo
    Distributed algorithms for big spatial and spatio-textual query processing
    Garcia-Muñoz, Raul; García-García, Francisco; Corral, Antonio. Actas de las XXVII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2023), 2023-09-12.
    A vast amount of geo-referenced data is generated daily by mobile devices, GPS-enabled devices, and other sensors, increasing the importance of spatio-textual analyses of such data. Big Spatio-Textual Data requires new distributed processing technologies for managing, storing, analyzing, and visualizing large-scale spatio-textual data. Distributed Spatio-Textual Data Management Systems (DSTDMSs) consist of shared nothing clusters of computers specifically designed for distributed processing of large-scale spatio-textual data. This paper presents our emerging work on designing new storage methods and query processing algorithms for Apache Sedona (a recent open-source in-memory cluster computing system for spatial data processing) to support batch and streaming spatio-textual data processing. Our research aims to incorporate new partitioning methods and indexing mechanisms that will help to implement new (static and continuous) spatio-textual queries, especially distance-based spatio-textual joins. Finally, we will evaluate the new proposals with exhaustive experiments over Apache Sedona as a DSTDMS, analyzing and drawing conclusions from the experimental result.
  • Artículo
    Distance Range Queries in SpatialHadoop
    Corral, Antonio; García-García, Francisco; Iribarne, Luis; Vassilakopoulos, Michael. Actas de las XXI Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2016), 2016-09-13.
    Efficient processing of Distance Range Queries (DRQs) is of great importance in spatial databases due to the wide area of applications. This type of spatial query is characterized by a distance range over one or two datasets. The most representative and known DRQs are the eDistance Range Query (eDRQ) and the eDistance Range Join Query (eDRJQ). Given the increasing volume of spatial data, it is difficult to perform a DRQ on a centralized machine efficiently. Moreover, the eDRJQ is an expensive spatial operation, since it can be considered a combination of the eDR and the spatial join queries. For this reason, this paper addresses the problem of computing DRQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently, and proposes new algorithms in SpatialHadoop to perform efficient parallel DRQs on large-scale spatial datasets. We have evaluated the performance of the proposed algorithms in several situations with big synthetic and real-world datasets. The experiments have demonstrated the efficiency (in terms of total execution time and number of distance computations) and scalability (in terms of epsilon values, sizes of datasets and number of computing nodes) of our proposal.