Giorgio Vinciguerra

According to our database, Giorgio Vinciguerra authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Grafite: Taming Adversarial Queries with Optimal Range Filters.

Marco Costa, Paolo Ferragina, Giorgio Vinciguerra

Proc. ACM Manag. Data, February, 2024

CoCo-trie: Data-aware compression and indexing of strings.

Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra

Inf. Syst., February, 2024

2023

On Nonlinear Learned String Indexing.

Paolo Ferragina, Marco Frasca, Giosuè Cataldo Marinò, Giorgio Vinciguerra

IEEE Access, 2023

Engineering a Textbook Approach to Index Massive String Dictionaries.

Paolo Ferragina, Mariagiovanna Rotundo, Giorgio Vinciguerra

Proceedings of the String Processing and Information Retrieval, 2023

Learned Monotone Minimal Perfect Hashing.

Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, Giorgio Vinciguerra

Proceedings of the 31st Annual European Symposium on Algorithms, 2023

2022

Learning-based compressed data structures.

Giorgio Vinciguerra

PhD thesis, 2022

A Learned Approach to Design Compressed Rank/Select Data Structures.

Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra

ACM Trans. Algorithms, 2022

Compressing and Querying Integer Dictionaries Under Linearities and Repetitions.

Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra

IEEE Access, 2022

Compressed String Dictionaries via Data-Aware Subtrie Compaction.

Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra

Proceedings of the String Processing and Information Retrieval, 2022

2021

On the performance of learned data structures.

Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra

Theor. Comput. Sci., 2021

Repetition- and Linearity-Aware Rank/Select Dictionaries.

Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra

Proceedings of the 32nd International Symposium on Algorithms and Computation, 2021

A "Learned" Approach to Quicken and Compress Rank/Select Dictionaries.

Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra

Proceedings of the Symposium on Algorithm Engineering and Experiments, 2021

2020

The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds.

Paolo Ferragina, Giorgio Vinciguerra

Proc. VLDB Endow., 2020

Why Are Learned Indexes So Effective?

Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The PGM-index: a multicriteria, compressed and learned approach to data indexing.

Paolo Ferragina, Giorgio Vinciguerra

CoRR, 2019

Superseding traditional indexes by orchestrating learning and geometry.

Giorgio Vinciguerra, Paolo Ferragina, Michele Miccinesi

CoRR, 2019

Learned Data Structures.

Paolo Ferragina, Giorgio Vinciguerra

Proceedings of the Recent Trends in Learning From Data, 2019