Fine-grained Pattern Matching Over Streaming Time Series

TL;DR

This paper introduces a novel approach for fine-grained pattern matching in streaming time series, enabling more precise and expressive matching with low latency and resource efficiency, suitable for Industry 4.0 applications.

Contribution

It formulates the new problem of fine-grained pattern matching and proposes a two-phase method with ELB representation and delta-function for efficient, exact matching.

Findings

Outperforms brute-force and MSM methods in experiments

Achieves low latency and resource-efficient matching

Effective pruning with no false dismissals

Abstract

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching problem, a pattern may contain multiple segments representing different statistical properties or physical meanings for more precise and expressive matching in real world. Hence, we formulate a new problem, called "fine-grained pattern matching", which allows users to specify varied granularities of matching deviation to different segments of a given pattern, and fuzzy regions for adaptive breakpoints determination between consecutive segments. In this paper, we propose a novel two-phase approach. In the pruning phase, we introduce Equal-Length Block (ELB) representation together with Block-Skipping Pruning (BSP) policy, which guarantees low cost feature calculation, effective pruning and no false dismissals. In the post-processing phase, a delta-function is proposed to enable us to conduct exact matching in linear complexity. Extensive experiments are conducted to evaluate on synthetic and real-world datasets, which illustrates that our algorithm outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation.