Giorgio Vinciguerra

31 papers A* 2A 3B 1C 2Journal 16Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learned Static Function Data Structures. Proc. VLDB Endow. Stefan Hermann, Hans-Peter Lehmann, Giorgio Vinciguerra, Stefan Walzer
2025	A	`conf`	Efficiency of Learned Indexes on Genome Spectra. ESA Md. Hasin Abrar, Paul Medvedev, Giorgio Vinciguerra
2025	A*	`conf`	Learned Compression of Nonlinear Time Series with Random Access. ICDE Andrea Guerra, Giorgio Vinciguerra, Antonio Boffa, Paolo Ferragina
2025	J	`jnl`	Learned Static Function Data Structures. CoRR Stefan Hermann, Hans-Peter Lehmann, Giorgio Vinciguerra, Stefan Walzer
2025	J	`jnl`	On the compressibility of large-scale source code datasets. J. Syst. Softw. Antonio Boffa, Roberto Di Cosmo, Paolo Ferragina, Andrea Guerra, Giovanni Manzini, Giorgio Vinciguerra, Stefano Zacchiroli
2025	—	`conf`	Reproducibility Report for ACM SIGMOD 2025 Paper: 'Mnemosyne: Dynamic Workload-Aware BF Tuning via Accurate Statistics in LSM trees'. SIGMOD ARI Reports Giorgio Vinciguerra, Tapan Srivastava, Zichen Zhu
2025	—	`conf`	Reproducibility Report for ACM SIGMOD 2025 Paper: 'Self-Enhancing Video Data Management System for Compositional Events with Large Language Models'. SIGMOD ARI Reports Giorgio Vinciguerra, Enhao Zhang
2025	J	`jnl`	Two-level massive string dictionaries. Inf. Syst. Paolo Ferragina, Mariagiovanna Rotundo, Giorgio Vinciguerra
2024	J	`jnl`	CoCo-trie: Data-aware compression and indexing of strings. Inf. Syst. Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra
2024	J	`jnl`	Grafite: Taming Adversarial Queries with Optimal Range Filters. Proc. ACM Manag. Data Marco Costa, Paolo Ferragina, Giorgio Vinciguerra
2024	J	`jnl`	Learned Compression of Nonlinear Time Series With Random Access. CoRR Andrea Guerra, Giorgio Vinciguerra, Antonio Boffa, Paolo Ferragina
2024	—	`conf`	Reproducibility Report for ACM SIGMOD 2024 Paper: 'DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance'. SIGMOD/PODS Giorgio Vinciguerra, Chuqing Gao, Shufan Zhang, Xi He
2024	—	`conf`	Reproducibility Report for ACM SIGMOD 2024 Paper: 'Grafite: Taming Adversarial Queries with Optimal Range Filters'. SIGMOD/PODS Carlos E. Muñiz-Cuza, Yin Lou, Kyoseung Koo, Marco Costa, Giorgio Vinciguerra
2024	—	`conf`	Reproducibility Report for ACM SIGMOD 2024 Paper: 'On The Reasonable Effectiveness of Relational Diagrams'. SIGMOD/PODS Giorgio Vinciguerra, Guang Yang, Wolfgang Gatterbauer, Cody Dunne
2023	C	`conf`	Engineering a Textbook Approach to Index Massive String Dictionaries. SPIRE Paolo Ferragina, Mariagiovanna Rotundo, Giorgio Vinciguerra
2023	J	`jnl`	Grafite: Taming Adversarial Queries with Optimal Range Filters. CoRR Marco Costa, Paolo Ferragina, Giorgio Vinciguerra
2023	A	`conf`	Learned Monotone Minimal Perfect Hashing. ESA Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, Giorgio Vinciguerra
2023	J	`jnl`	Learned Monotone Minimal Perfect Hashing. CoRR Paolo Ferragina, Hans-Peter Lehmann, Peter Sanders, Giorgio Vinciguerra
2023	J	`jnl`	On Nonlinear Learned String Indexing. IEEE Access Paolo Ferragina, Marco Frasca, Giosuè Cataldo Marinò, Giorgio Vinciguerra
2022	J	`jnl`	A Learned Approach to Design Compressed Rank/Select Data Structures. ACM Trans. Algorithms Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra
2022	C	`conf`	Compressed String Dictionaries via Data-Aware Subtrie Compaction. SPIRE Antonio Boffa, Paolo Ferragina, Francesco Tosoni, Giorgio Vinciguerra
2022	J	`jnl`	Compressing and Querying Integer Dictionaries Under Linearities and Repetitions. IEEE Access Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra
2022	—	`—`	Learning-based compressed data structures. Giorgio Vinciguerra
2021	A	`conf`	A "Learned" Approach to Quicken and Compress Rank/Select Dictionaries. ALENEX Antonio Boffa, Paolo Ferragina, Giorgio Vinciguerra
2021	J	`jnl`	On the performance of learned data structures. Theor. Comput. Sci. Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra
2021	B	`conf`	Repetition- and Linearity-Aware Rank/Select Dictionaries. ISAAC Paolo Ferragina, Giovanni Manzini, Giorgio Vinciguerra
2020	J	`jnl`	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. Proc. VLDB Endow. Paolo Ferragina, Giorgio Vinciguerra
2020	A*	`conf`	Why Are Learned Indexes So Effective? ICML Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra
2019	—	`conf`	Learned Data Structures. INNSBDDL (Tutorials) Paolo Ferragina, Giorgio Vinciguerra
2019	J	`jnl`	Superseding traditional indexes by orchestrating learning and geometry. CoRR Giorgio Vinciguerra, Paolo Ferragina, Michele Miccinesi
2019	J	`jnl`	The PGM-index: a multicriteria, compressed and learned approach to data indexing. CoRR Paolo Ferragina, Giorgio Vinciguerra