Search Results for author: Paolo Ferragina

Found 8 papers, 4 papers with code

Why Are Learned Indexes So Effective?

1 code implementation ICML 2020 Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra

A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends in the input data so to achieve outstanding practical improvements in space occupancy and time efficiency.

An interactive dashboard for searching and comparing soccer performance scores

no code implementations16 Apr 2021 Paolo Cintia, Giovanni Mauro, Luca Pappalardo, Paolo Ferragina

The performance of soccer players is one of most discussed aspects by many actors in the soccer industry: from supporters to journalists, from coaches to talent scouts.

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

1 code implementation PVLDB, 13(8) 2020 Paolo Ferragina, Giorgio Vinciguerra

We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case.


Superseding traditional indexes by orchestrating learning and geometry

no code implementations1 Mar 2019 Giorgio Vinciguerra, Paolo Ferragina, Michele Miccinesi

We design the first learned index that solves the dictionary problem with time and space complexity provably better than classic data structures for hierarchical memories, such as B-trees, and modern learned indexes.

Data Structures and Algorithms E.1; E.4; I.2.6

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

no code implementations10 May 2018 Paolo Cifariello, Paolo Ferragina, Marco Ponza

Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia.

Entity Linking Language Modelling

SWAT: A System for Detecting Salient Wikipedia Entities in Texts

no code implementations10 Apr 2018 Marco Ponza, Paolo Ferragina, Francesco Piccinno

We study the problem of entity salience by proposing the design and implementation of SWAT, a system that identifies the salient Wikipedia entities occurring in an input document.

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

1 code implementation14 Feb 2018 Luca Pappalardo, Paolo Cintia, Paolo Ferragina, Emanuele Massucco, Dino Pedreschi, Fosca Giannotti

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e. g., tackles, passes, shots, etc.).

BIG-bench Machine Learning

Compressed Text Indexes:From Theory to Practice!

6 code implementations20 Dec 2007 Paolo Ferragina, Rodrigo Gonzalez, Gonzalo Navarro, Rossano Venturini

A compressed full-text self-index represents a text in a compressed form and still answers queries efficiently.

Data Structures and Algorithms F.2.2; H.2.1; H.3.2; H.3.3

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