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Massimo Cafaro

Associate Professor

University of Salerno

Italy

Research Interests

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Professors in Italy Professors in Biostatistics Biostatistics professors in Italy

About

Massimo Cafaro is an Associate Professor at the University of Salento, Italy. His research areas encompass data stream analysis, quantile tracking, and advanced information technology solutions. He has published recent articles on various topics, including adaptive phase detrending, heavy hitters in data streams, and the future of internet technology in Italy. His work contributes significantly to the fields of data science and distributed computing.

Articles (16)

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2027

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2026

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2025

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2024

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2024

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2024

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2022

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2022

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2022

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2022

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2021

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2020

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2020

Collaborators (6)

Antonio Cicone

Associate Professor

University of L'Aquila

ITALY

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Marco Pulimeno

Associate Professor

Università del Salento

ITALY

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Lucas Pereira

Research Faculty

Universidade de Aveiro

PORTUGAL

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Hugo Morais

Universidade de Aveiro

PORTUGAL

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Italo Epicoco

Assistant Professor

Università del Salento

ITALY

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Jinoh Kim

Faculty

Texas A&M University Commerce

UNITED STATES

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Massimo Cafaro

Research Interests

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Articles (16)

Fragility Fracture Risk Prediction through the integration of Artificial Intelligence and Radiofrequency Echographic Multi Spectrometry (REMS) Technology

Big data energy systems: A survey of practices and associated challenges

The Design and Implementation of a Graph-Based P2P Data Storage Service

WATCH '25: First Workshop on Analytics, Telemetry, and Cybersecurity for HPCC

Parallel and Distributed Frugal Tracking of a Quantile

State-of-the-Art Future Internet Technology in Italy 2022–2023

Just in Time Transformers

Toward Enhanced Support for Ship Sailing

Data stream fusion for accurate quantile tracking and analysis

Adaptive Phase Detrending for GNSS Scintillation Detection: A Case Study Over Antarctica

Deterministic, Fast and Accurate Solution of the Heavy Hitters q-Tail Latencies Problem

Deterministic, Fast and Accurate Solution of the Heavy Hitters <i>q</i>-Tail Latencies Problem

AFQN: approximate Qn estimation in data streams

Distributed mining of time-faded heavy hitters

On Frequency Estimation and Detection of Heavy Hitters in Data Streams

UDDSketch: Accurate Tracking of Quantiles in Data Streams

Collaborators (6)

Antonio Cicone

Marco Pulimeno

Lucas Pereira

Hugo Morais

Italo Epicoco

Jinoh Kim

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