Listar por autor "Gagie, Travis"

Mostrando ítems 1-5 de 5

    • An index for moving objects with constant-time access to their compressed trajectories 

      Brisaboa, Nieves R.; Gagie, Travis; Gómez-Brandón, Adrián; Navarro, Gonzalo; Paramá, José R. (Taylor & Francis, 2021)
      [Abstract]: As the number of vehicles and devices equipped with GPS technology has grown explosively, an urgent need has arisen for time- and space-efficient data structures to represent their trajectories. The most commonly ...

    • Augmented Thresholds for MONI 

      Martínez-Guardiola, César; Brown, Nathaniel K.; Silva-Coira, Fernando; Köppl, Dominik; Gagie, Travis; Ladra, Susana (Institute of Electrical and Electronics Engineers Inc., 2023)
      [Abstract]: MONI (Rossi et al., 2022) can store a pangenomic dataset T in small space and later, given a pattern P, quickly find the maximal exact matches (MEMs) of P with respect to T. In this paper we consider its one-pass ...

    • Efficient and Compact Representations of Some Non-canonical Prefix-Free Codes 

      Fariña, Antonio; Gagie, Travis; Manzini, Giovanni; Navarro, Gonzalo; Ordóñez, Alberto (Springer, 2016-09-21)
      [Abstract] For many kinds of prefix-free codes there are efficient and compact alternatives to the traditional tree-based representation. Since these put the codes into canonical form, however, they can only be used when ...

    • Exploiting Computation-Friendly Graph Compression Methods for Adjacency-Matrix Multiplication 

      Francisco, Alexandre P.; Gagie, Travis; Ladra, Susana; Navarro, Gonzalo (IEEE Computer Society, 2018-03)
      [Abstract] Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In ...

    • Graph Compression for Adjacency-Matrix Multiplication 

      Francisco, Alexandre P.; Gagie, Travis; Köppl, Dominik; Ladra, Susana; Navarro, Gonzalo (Springer, 2022)
      [Abstract] Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In ...