Distributed Seasonal Temporal Pattern Mining

TL;DR

This paper introduces DSTPM, a distributed framework for efficiently mining seasonal temporal patterns in large-scale time series data, overcoming scalability issues of traditional methods.

Contribution

The paper presents the first distributed approach for seasonal temporal pattern mining, utilizing hierarchical lookup hash structures for improved efficiency and scalability.

Findings

DSTPM outperforms sequential methods in runtime and memory.

DSTPM scales effectively to very large datasets.

Experimental results validate the efficiency of the proposed framework.

Abstract

The explosive growth of IoT-enabled sensors is producing enormous amounts of time series data across many domains, offering valuable opportunities to extract insights through temporal pattern mining. Among these patterns, an important class exhibits periodic occurrences, referred to as \textit{seasonal temporal patterns} (STPs). However, mining STPs poses challenges, as traditional measures such as support and confidence cannot capture seasonality, and the lack of the anti-monotonicity property results in an exponentially large search space. Existing STP mining methods operate sequentially and therefore do not scale to large datasets. In this paper, we propose the Distributed Seasonal Temporal Pattern Mining (DSTPM), the first distributed framework for mining seasonal temporal patterns from time series. DSTPM leverages efficient data structures, specifically distributed hierarchical lookup hash structures, to enable efficient computation. Extensive experimental evaluations demonstrate that DSTPM significantly outperforms sequential baselines in runtime and memory usage, while scaling effectively to very large datasets.