Publications

Marco Costa, Paolo Ferragina, Giorgio Vinciguerra (2024). Grafite: taming adversarial queries with optimal range filters. PACMMOD.

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Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra (2024). CoCo-trie: data-aware compression and indexing of strings. Inf. Syst..

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Paolo Ferragina, Mariagiovanna Rotundo, Giorgio Vinciguerra (2023). Engineering a textbook approach to index massive string dictionaries. SPIRE.

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Paolo Ferragina, Marco Frasca, Giosuè Cataldo Marinò, Giorgio Vinciguerra (2023). On nonlinear learned string indexing. IEEE Access.

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Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, Giorgio Vinciguerra (2023). Learned monotone minimal perfect hashing. ESA.

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Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra (2022). Compressing and querying integer dictionaries under linearities and repetitions. IEEE Access.

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Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra (2022). Compressed string dictionaries via data-aware subtrie compaction. SPIRE.

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Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra (2022). A learned approach to design compressed rank/select data structures. ACM Trans. Algorithms.

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Giorgio Vinciguerra (2022). Learning-based compressed data structures. Ph.D. thesis.

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Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra (2021). Repetition- and linearity-aware rank/select dictionaries. ISAAC.

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Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra (2021). On the performance of learned data structures. Theor. Comput. Sci..

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Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra (2021). A “learned” approach to quicken and compress rank/select dictionaries. ALENEX.

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Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra (2020). Why are learned indexes so effective?. ICML.

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Paolo Ferragina, Giorgio Vinciguerra (2020). The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. PVLDB.

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Paolo Ferragina, Giorgio Vinciguerra (2020). Learned data structures. Recent Trends in Learning From Data (Springer).

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