28 citations · 50 across the 9 of their papers we have counts for
13 papers
Diffusion-Based Super-Resolution of Adriatic Sea Oceanographic Fields
Rajat Srivastava, Muhammad Sarmad, Emanuele Mele +3
High-resolution oceanographic fields are critical for resolving mesoscale and sub-mesoscale coastal dynamics, yet their generation remains constrained by both computational cost an…
Space-Efficient Private Estimation of Quantiles
Massimo Cafaro, Aneglo Coluccia, Italo Epicoco +1
Fast and accurate estimation of quantiles on data streams coming from communication networks, Internet of Things (IoT), and alike, is at the heart of important data processing appl…
Just In Time Transformers
Ahmed Ala Eddine Benali, Massimo Cafaro, Italo Epicoco +2
Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility c…
Data stream fusion for accurate quantile tracking and analysis
Massimo Cafaro, Catiuscia Melle, Italo Epicoco +1
UDDSKETCH is a recent algorithm for accurate tracking of quantiles in data streams, derived from the DDSKETCH algorithm. UDDSKETCH provides accuracy guarantees covering the full ra…
UDDSketch: Accurate Tracking of Quantiles in Data Streams
Italo Epicoco, Catiuscia Melle, Massimo Cafaro +2
We present UDDSketch (Uniform DDSketch), a novel sketch for fast and accurate tracking of quantiles in data streams. This sketch is heavily inspired by the recently introduced DDSk…
Fast Detection of Outliers in Data Streams with the Estimator
Massimo Cafaro, Catiuscia Melle, Marco Pulimeno +1
We present FQN (Fast ), a novel algorithm for fast detection of outliers in data streams. The algorithm works in the sliding window model, checking if an item is an outlier by…
Distributed mining of time--faded heavy hitters
Marco Pulimeno, Italo Epicoco, Massimo Cafaro
We present \textsc{P2PTFHH} (Peer--to--Peer Time--Faded Heavy Hitters) which, to the best of our knowledge, is the first distributed algorithm for mining time--faded heavy hitters…
Mining frequent items in unstructured P2P networks
Massimo Cafaro, Italo Epicoco, Marco Pulimeno
Large scale decentralized systems, such as P2P, sensor or IoT device networks are becoming increasingly common, and require robust protocols to address the challenges posed by the…
Parallel mining of time-faded heavy hitters
Massimo Cafaro, Marco Pulimeno, Italo Epicoco
We present PFDCMSS, a novel message-passing based parallel algorithm for mining time-faded heavy hitters. The algorithm is a parallel version of the recently published FDCMSS seque…
Fast and Accurate Mining of Correlated Heavy Hitters
Italo Epicoco, Massimo Cafaro, Marco Pulimeno
The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra--G…
Parallel Space Saving on Multi and Many-Core Processors
Massimo Cafaro, Marco Pulimeno, Italo Epicoco +1
Given an array of elements and a value , a frequent item or -majority element is an element occurring in more than times.…
Mining frequent items in the time fading model
Massimo Cafaro, Marco Pulimeno, Italo Epicoco +1
We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and…
A Parallel Space Saving Algorithm For Frequent Items and the Hurwitz zeta distribution
Massimo Cafaro, Marco Pulimeno, Piergiulio Tempesta
We present a message-passing based parallel version of the Space Saving algorithm designed to solve the --majority problem. The algorithm determines in parallel frequent items,…