# Parallel mining of time-faded heavy hitters

## TL;DR

This paper introduces PFDCMSS, a parallel algorithm for efficiently mining time-faded heavy hitters that maintains high accuracy and scalability on message-passing architectures.

## Contribution

It presents the first parallel algorithm for time-faded heavy hitters that is mergeable, accurate, and scalable, based on a novel augmented sketch data structure.

## Key findings

- Retains the accuracy and error bounds of the sequential FDCMSS algorithm.
- Achieves excellent parallel scalability on message-passing architectures.
- Proves the mergeability of the augmented sketch data structure.

## Abstract

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 sequential algorithm. We formally prove its correctness by showing that the underlying data structure, a sketch augmented with a Space Saving stream summary holding exactly two counters, is mergeable. Whilst mergeability of traditional sketches derives immediately from theory, we show that merging our augmented sketch is non trivial. Nonetheless, the resulting parallel algorithm is fast and simple to implement. To the best of our knowledge, PFDCMSS is the first parallel algorithm solving the problem of mining time-faded heavy hitters on message-passing parallel architectures. Extensive experimental results confirm that PFDCMSS retains the extreme accuracy and error bound provided by FDCMSS whilst providing excellent parallel scalability.