# Distributed mining of time--faded heavy hitters

## TL;DR

This paper introduces P2PTFHH, a distributed algorithm for mining time-faded heavy hitters in P2P networks, combining gossip protocols with sketch data structures to achieve high accuracy and scalability.

## Contribution

It is the first distributed method for time-faded heavy hitter mining in unstructured P2P networks, integrating gossip protocols with augmented sketch data structures.

## Key findings

- Retains high accuracy and error bounds of FDCMSS
- Proves convergence and correctness of the distributed algorithm
- Demonstrates near real-time processing capability on large datasets

## Abstract

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 on unstructured P2P networks. \textsc{P2PTFHH} is based on the \textsc{FDCMSS} (Forward Decay Count--Min Space-Saving) sequential algorithm, and efficiently exploits an averaging gossip protocol, by merging in each interaction the involved peers' underlying data structures. We formally prove the convergence and correctness properties of our distributed algorithm and show that it is fast and simple to implement. Extensive experimental results confirm that \textsc{P2PTFHH} retains the extreme accuracy and error bound provided by \textsc{FDCMSS} whilst showing excellent scalability. Our contributions are three-fold: (i) we prove that the averaging gossip protocol can be used jointly with our augmented sketch data structure for mining time--faded heavy hitters; (ii) we prove the error bounds on frequency estimation; (iii) we experimentally prove that \textsc{P2PTFHH} is extremely accurate and fast, allowing near real time processing of large datasets.