Towards Distributed Convoy Pattern Mining

TL;DR

This paper addresses the challenge of scalable convoy pattern mining in movement data by analyzing data partitioning strategies to enable a generic distributed algorithm.

Contribution

It introduces an analysis of data partitioning strategies crucial for developing a scalable distributed convoy pattern mining algorithm.

Findings

Analyzed various data partitioning strategies for convoy mining.

Identified key challenges in scaling convoy pattern detection.

Proposed directions for a generic distributed convoy mining algorithm.

Abstract

Mining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however do not scale to real-life dataset sizes. Therefore a distributed algorithm for convoy mining is inevitable. In this paper, we discuss the problem of convoy mining and analyze different data partitioning strategies to pave the way for a generic distributed convoy pattern mining algorithm.